Collateral Requirements and Corporate Policy Decisions∗†‡

Kizkitza Biguri§

BI Norwegian Business School

Jorg Stahl¶

Catolica Lisbon School of Business & Economics

September 19, 2019

Abstract

We study how collateral requirements affect corporate policy decisions and present evidence

that challenges the trade-off theory between risk management and investment policy due to

collateral constraints. We compile a novel dataset on collateral and derivative transactions for

all U.S. public firms. We show that cash plays a dual role as liquidity management instrument

and the main source of collateral for derivative transactions. Exploiting exogenous variation in

liquidity and real estate collateral values, we show that cash-collateral constraints are binding for

risk management decisions, while the decisions are unaffected by variations in real estate prices.

We find a 8% reduction in hedging and a 0.1% reduction in cash-collateral pledged.

∗JEL Codes: G01, G31, G32, G39.†Keywords: Collateral, Hedging, Risk Management, Liquidity, Financial constraints, Net worth, Investment.‡Luca Brogin and Vegar Skaret provided excellent research assistance. We thank Maximilian Rohrer for helping us

build the derivatives collateral database. We also thank Gene Ambrocio, Ettore Croci (discussant), Miguel de Jesus,Ruediger Fahlenbrach, Itay Goldstein, Scott Guernsey (discussant), Iftekhar Hasan, Dmitry Khametshin, ØyvindNorli, Felix Noth, Steven Ongena, Nick Pretnar, Denis Sosyura, Jerome Taillard (discussant), Jake Thornock, MayaWaisman (discussant), Adalbert Winkler, the participants of the European Finance Association Meeting, CEPR-2nd

Endless Summer Conference of Financial Intermediation and Corporate Finance, 6th Emerging Scholars in Bankingand Finance at Cass Business School, the 2018 Paris Financial Management Conference, the 43rd Simposio de laAsociacion Espanola de Economıa, the 2nd Young Writers’ Workshop at Bonn University, the BI Accounting Brownbagseminar, INFINITI, CUNEF and the 27th Finance Forum for useful comments. We are grateful to Michael Kobida,Executive Director of Collateral Services Program at the CMEGroup, for providing information on the collateralizationpractices for exchange-traded derivatives. Kizkitza Biguri gratefully acknowledges the financial support of the “BI BasicResearch Grant, 2016”. Jorg Stahl gratefully acknowledges funding by grants UID/GES/00407/2013 and PTDC/EGE-OGE/30314/2017 of the Portuguese Foundation for Science and Technology-FCT. All remaining errors are ours. Findthe latest draft here!§E-mail: kizkitza.biguri@bi.no. Phone: +47 46410566. Address: BI Norwegian Business School, Office A5-031,

Nydalsveien, 37, 0484 Oslo, Norway.¶E-mail: jstahl@ucp.pt. Catolica Lisbon School of Business & Economics, 1649-023 Lisboa, Portugal.

1 Introduction

Regulators view collateralization as one of the main mechanisms to reduce systemic risk and to

avoid severe economic disruptions in financial markets associated with derivative transactions.1

Derivatives even offer special protection; in the case of bankruptcy, counterparties can seize and

sell collateral for derivatives before creditors (Bolton and Oehmke (2015)).2 Collateralization has

also facilitated the democratization of the derivatives market. Initially, derivatives were limited to

high credit quality firms, but collateralization has given firms with lower credit quality access to

the derivatives market (Bliss and Kaufman (2006)). The effects of the 2007 financial crisis, with

large financial institutions failing, and the posterior liquidity crunch has contributed to a further

rapid growth in the use of collateralization in the derivatives market in recent years.3 Surprisingly,

research on collateralization in risk management for non-financial corporations remains scant.

Collateral is generally viewed as property, plant and equipment (PPE) of the firm.4 Accord-

ingly, firms face a trade-off between financing and risk management decisions, as both promises

have to be collateralized. From their theoretical analysis, Rampini et al. (2014) conclude that fi-

nancially constrained firms prioritize collateral for investment purposes and thus, they hedge less

or do not hedge at all.5 However, that firms pledge other assets in addition to or in substitution

of PPE in order to secure financing (Degryse et al. (2014), Liberti and Mian (2010)). Importantly,

PPE as collateral plays a negligible role for derivative transactions. The 2018 International Swaps

and Derivatives Association (ISDA) “Margins Survey” states that out of total collateral pledged in

derivative transactions, 96.4% is cash and securities and 3.6% is PPE.6 This evidence suggests that

we still know very little about how collateralization works in practice and about its effects. In par-

ticular, a collateral-constraints-induced trade-off between financing and risk management decisions

is not obvious if their sources of collateral differ. To the best of our knowledge, no prior research

has explicitly analyzed the sources and valuation of collateral in derivative transactions (and debt)

for non-financial corporations and how they affect corporate policy decisions. This paper aims to

fill this gap in the literature.

1See Acharya and Bisin (2014), BIS (2014), BIS (2013b), Heller and Vause (2012), IMF (2010).2The close-out clause permits the immediate termination of all contracts under the same master agreement between

the solvent and the insolvent party. Executory contracts (derivatives) that can be terminated, while non-executorycontracts (financial debt) can only be accelerated upon default. See Johnson (2000) for termination events.

3In April 2009, the G20 nations made a commitment to promote the standardization and resilience of over-the-counter (OTC) derivatives markets, in particular through the establishment of central clearing counterparties (CCP)subject to effective regulation and supervision. Dodd-Frank in 2010 contains key provisions for the U.S., while theEuropean Market Infrastructure Reguation (EMIR) is the correspondence in the E.U.

4See Rampini et al. (2014), Rampini and Viswanathan (2010) and Chaney et al. (2012)5Empirically, they test the relation between the intensive margin of hedging and credit quality to find a positive

relation and show how firms reduce hedging around distress. However, they use no data on collateral requirements.6See ISDA’s Margin Survey 2018. This pattern has been consistent over time for OTC derivatives (ISDA (1999)),

but also for exchange-traded derivatives based on private conversations with the CMEGroup.

1

We simultaneously use manual collection of data and a text-search algorithm to construct a

dataset on U.S. public firms’ collateral requirements for risk management and financing purposes

from 1996 to 2015.7 We identify the sources of collateral, the position for derivatives (pledged vs.

received) and the value of the collateral. We also have information on financial hedging for these

firms across time.8 The main purpose of the paper is to analyze whether collateral constraints are

binding for risk management decisions and to shed light on how collateral requirements for both,

risk management and financial decisions, affect corporate policy. The main result of the paper is

that there is no trade-off between risk management and investment due to collateral requirements.9

We instead find that cash plays a dual role as liquidity management instrument and as the main

source of collateral to alleviate counterparty risk in derivative transactions. We analyze the effect of

an exogenous negative shock to liquidity and observe that risk management activities are reduced

as a result. Thus, a complementary relation between cash and hedging arises for firms pledging

collateral for derivatives. Finally, we also provide implications for the recent regulatory changes in

the over-the-counter (OTC) derivatives market.

Our work offers several interesting results. First, we build on the definitions for financial con-

straints in Almeida et al. (2004) and Hadlock and Pierce (2010). Sorting by these definitions, we

conclude that financially constrained firms generally hedge less, which is consistent with existing

literature. We show, however, that the lower likelihood to engage in risk management is not driven

by collateral requirements in debt contracts. Firms with a collateral requirement for debt report

hedging rates that are 15-26% higher.

We then focus on firm characteristics of companies that have both collateral requirements for debt

and/or derivatives. We show that firms pledging collateral for debt (derivatives) tend to be smaller

(larger), have lower (higher) investment opportunities, leverage, and investment and higher (lower)

cash holdings. The lower cash holdings for firms pledging collateral for derivatives can be rationalized

through the characteristics of hedging firms. Hedging helps reducing exposure to systematic risk,

which leads to a substitute relation with liquidity (Disatnik et al. (2013)). However, cash holdings

must mitigate the remaining idiosyncratic risk exposures and thus, motivate a complementarity

relation, which interlinks with other corporate policy decisions (Bolton et al. (2011)). In line with

this reasoning, our results suggest that collateral-pledging hedgers hold higher cash holdings than

7Throughout the paper we use the terms risk management and financial hedging indistinctly. Unfortunately, welack data on natural or operational hedges performed by firms at the moment.

8We focus on the use of derivatives for hedging purposes only, not for speculative purposes. We identify the maintypes of financial hedges (i.e. foreign exchange, interest rate and commodity price) and the types of instruments used(i.e. futures, forwards, options, swaps and other instruments).

9Froot et al. (1993) and Carter et al. (2006a) also find no evidence supporting the trade-off, while Jankensgard(2017) and Rampini et al. (2014) find supporting evidence.

2

unsecured hedgers or those with a collateral credit trigger.10 Moreover, we show that as credit quality

decreases, the pledge of collateral for derivative transactions becomes more likely, strengthening the

complementary relation further.

To shed light on the complementary relation between cash and hedging, we analyze the effect

of a negative liquidity shock. The relation between hedging and cash holdings is subject to clear

endogeneity issues. In order to alleviate the concerns, we use a shock to the liquidity of firms

that brings cash holdings temporarily out of the optimal level. We exploit exogenous variation in

cashflows caused by hurricane Katrina in 2005, to analyze the effects of the shock on affected firms’

risk management decisions (similar to Dessaint and Matray (2017)).11 More precisely, we use a

difference-in-differences (DID) setup to study the response of the extensive margin of hedging. We

conjecture that firms located in Katrina states (treatment firms) reduce hedging comparatively more

than other firms not affected by hurricane Katrina (control firms). The underlying assumption is

that the reduction in hedging is driven by the existence of cash-collateral requirements for derivative

contracts and thus, collateral constraints are binding for risk management. For robustness, we also

analyze the response of collateral pledged.

The coefficients from the DID estimation are negative and statistically significant. Firms in

Katrina states reduce hedging by 8% (2%) more than firms in non-Katrina states in a propensity

score matched (unmatched) sample as a result of the liquidity shock (a 4% drop in average cash

holdings). The results are consistent with the conjecture that cash plays a dual role: it is a liquidity

management instrument, but also the main source of collateral to alleviate counterparty risk for

derivative transactions. The results also reinforce the hypothesis that cash holdings and hedging have

a complementary relation. To the extent that firms financially constrained can access the derivatives

market by pledging cash-collateral, liquidity and risk management decisions are inevitably linked

(Keynes (1936)). We also find a decrease in collateral pledged over total assets of 0.1% for firms in

the treatment group. However, we do not observe any effect on investment decisions.

While treating derivative transactions as homogenous has the advantage of simplicity, we also

analyze the DID response of different types of derivatives, which may also imply different collateral

requirements. More precisely, some risk management strategies may require a cash outflow (i.e.

long position on put option), while other strategies like linear strategies or some collars or forward

contracts do not. Our results suggest that the reduction in hedging we observe comes from firms

10Section 3.1 discusses the collateralization process for derivative transactions in detail.11We relate to Dessaint and Matray (2017) in that we are using hurricanes as a shock to liquidity for firms located

in the disaster area. However, our setup differs from theirs in that we use one of the hurricane events only and wewant to mute the PPE component of the shock. For this purpose, we use an accounting trick to rule out those casesin which a sizable shock to PPE also takes place as a result from Katrina. We exclude firms that report an assetimpairment because of the hurricane, consistent with U.S. GAAP ASC-360 “Property, Plant and Equipment”.

3

located in the Katrina states relying less on foreign exchange derivatives (by exposure) and futures

and options (by type of instrument). Our results are aligned with the mandatory collateral require-

ments in terms of initial and variation margin for exchange-traded derivatives. Moreover, we do

not observe a reduction in those instruments that are generally transacted in OTC markets such as

forward contracts. These results provide further suggestive evidence on cash-collateral constraints

being the mechanism behind the reduction in hedging.

We perform several robustness checks. First, although PPE is merely a residual source of col-

lateral to secure derivative transactions, we want to make sure that there is no effect operating via

collateralization from PPE holdings to risk management practices. For example, when firms receive

a margin call, they could ask for secured external financing, collateralized by PPE, to satisfy the

margin call. Building on the work by Chaney et al. (2012) on the identification of the collateral

channel for investment, we evaluate the effect of variation in local real estate prices on the exten-

sive margin of hedging for U.S. public firms holding real estate. We find no statistically significant

effect of local real estate prices on risk management decisions, while we find the usual positive and

statistically significant effect on firms’ investment. Second, we perform a propensity score matching

strategy in the Katrina identification (as in Michaely and Roberts (2012)). We require treatment

and control to have similar firm characteristics to ensure the conditional independence assumption

is satisfied (no selection bias in our observed average response of hedging or collateral requirements).

We derive the same conclusions as with the unmatched sample, but results are economically more

relevant. Third, we want to make sure that the way we define our identification strategy does not

mechanically affect corporate hedging decisions or collateral requirements. We, therefore, perform a

placebo test for the Katrina event by arbitrarily moving the date of the hurricane to fiscal year 2002.

We find no statistically significant results. Fourth, we look at whether collateral requirements for

derivatives indeed affect firms’ level of cash holdings following Opler et al. (1999). We find that a one

standard deviation increase in collateral requirements for derivatives by counterparties reduces cash

holdings by 0.3%. Finally, we gather collateral requirements in the Rampini et al. (2014) sample

and conclude that the trade-off between investment and risk management does not seem to occur

in their sample.

This work makes several contributions to the literature. First, it provides a new perspective

on the interaction of different corporate policy decisions in the context of collateralization. To

the best of our knowledge, this is the first paper using explicit data on collateral requirements for

financing and risk management decisions to investigate their interaction along with implications for

liquidity and other corporate policy decisions. The closest paper to ours is Bolton et al. (2011),

which proposes a model of dynamic investment, financing, and risk management for financially

4

constrained firms. We complement their analysis by providing empirical evidence on their model’s

predictions in the context of collateral requirements for derivatives and intertwined corporate policy

decisions. We also add to the results of earlier work on the determinants of risk management and its

effect on firm value and corporate policy decisions (Nance et al. (1993), Froot et al. (1993), Smith

and Stulz (1985), Mian (1996), Tufano (1998), Opler et al. (1999), Jin and Jorion (2006), Cornaggia

(2013), Perez-Gonzalez and Yun (2013), Gilje and Taillard (2017)). Second, it provides evidence on a

complementary relation between cash and hedging, supplementing previous findings in the literature

(Disatnik et al. (2013)) and consistent with theoretical predictions in Bolton et al. (2011). Third, the

results add to the recent literature analyzing the factors leading to the large increase in cash holdings

for U.S. corporations (Graham and Leary (2018), Bates et al. (2009)). Although further analysis

would be needed, collateralization in derivative transaction may well be a factor behind part of the

observed increase. Finally, it offers the context and the necessary data to study the implications

of recent changes in the regulation of OTC derivatives and adds to the growing literature on the

post-crisis derivatives regulatory framework.12 Extending regulation in OTC derivative markets to

include all non-financial corporations through higher demand for collateral, a more stringent pool of

eligible collateral and/or higher haircuts being implemented, would most likely resemble the effects

of the shock to liquidity in the Katrina identification strategy.

The rest of the paper is structured as follows. Section 2 discusses the identification strategy

and the methodology. In Section 3 we describe the functioning of collateralization in derivative

transactions, the data, and the variables used in the analysis. Section 4 provides the main empirical

analysis and results and relates the results to implications of recent regulatory changes in the OTC

derivatives market. Section 5 presents some robustness checks and Section 6 concludes.

2 Identification and Methodology

Our identification strategy relates to the setup in Dessaint and Matray (2017), in which hurricane

events affect firms located in the neighborhood of a disaster area.13 Our work focuses on the

effect hurricane Katrina in 2005, for which total property damage caused was estimated at $125

billion (2005 USD). Figure 1 shows Katrina’s track in the U.S. We identify Florida, Louisiana and

Mississippi (Katrina states) as the three states most affected by hurricane Katrina.14

12The majority of this line of research in part of the banking literature. Silva-Araujo and Leao (2016) offer anempirical analysis of the indirect effects passed by financial institutions to non-financial corporations of OTC derivativeregulation, while Mello and Parsons (2013) provide a descriptive analysis of the (lack of) direct effect.

13They study managers’ reactions to hurricane events when their firms are located in the neighborhood of thedisaster area and they experience an increase in perceived liquidity risk. They use a panel of hurricane events between1989 and 2008 in their analysis.

14The official Atlantic hurricane database (HURDAT) includes information on all officially recorded tropical cyclonesin the U.S. and their trajectory. The information is provided by the National Oceanic and Atmospheric Administra-tion’s Hurricane Research Division.

5

Exposed firms may experience losses due to asset impairments (shock to PPE) or lost business

and profitability and/or decreased productivity (shock to cashflows). Hurricanes are not considered

unexpected events from an accounting perspective. Therefore, the entire impact of the hurricane

ends up being reflected in cashflows and eventually, in the cash holdings of the firm. Our setup

differs from the Dessaint and Matray (2017) setup in that we restrict our analysis to those cases for

which there are no property claims. Ideally, we would like to focus only on the cashflow component

of the shock, while muting the PPE component to avoid confounding effects. As a result, we exclude

firms that reported an asset impairment as a result of Katrina in 2005 from the treatment group.

Our identifying assumption is that hurricane Katrina acts as a random and unexpected liquidity

shock to firms located in Katrina states (treatment group 1). However, assigning treatment at a

state-level may be imprecise. For instance, hurricane Katrina only impacted the south of Florida

and thus, assigning firms located in the north of Florida may lead to inaccurate estimates of the

effect of treatment. Therefore, for robustness, we build a second definition of treatment that assigns

only firms located in cities that were affected by hurricane Katrina (treatment group 2).15

The remaining firm-year observations are part of the control group. We define the post-treatment

period as any firm-year observation in fiscal year 2005 and the pre-treatment period as the obser-

vations in fiscal years 2002-04. We limit the post-treatment period to fiscal year 2005 to avoid

confounding effects from insurance claims made by companies. There are examples of companies

already receiving proceeds from insurance companies in fiscal year 2006.16 Finally, we ensure one

firm-year observation in the pre- and post-treatment periods to avoid attrition.

When hit by hurricane Katrina, the cashflows of firms in the treatment group reduce as compared

to the control group, and this brings cash holdings temporarily out of the optimal level. Figure 2

shows the pre- and post-treatment average cash holdings for the treatment and control groups. The

graph suggests that average cash holdings go down by 4% for firms in the treatment group in Katrina

states as compared to the control group. We precisely use this negative exogenous shock to liquidity

for identification purposes.

Our hypothesis is that firms use cash and marketable securities as a source of collateral for

derivative transactions. Therefore, when a negative liquidity shock brings cash holdings out of the

optimal level, firms need to re-optimize the uses of cash (and corporate policy decisions). At the

margin, firms may find themselves unable to sustain derivative contracts after the shock due to

15Two other hurricanes hit the U.S. in fiscal year 2005. Hurricane Rita hit Louisiana and Texas in September, whilehurricane Wilma hit the south of Florida in October. Our identification strategy already considers the possible impactof hurricane Rita in Louisiana and Wilma in south Florida. The main effect of Rita in Texas was in Beaumont. Werule out the unique firm which is located at Beaumont (Firstplus Financial Group Inc. (SIC code 5500)).

16See the following two examples for a company that already received insurance payments in March 2007 (fiscal year2006) and a company that did not receive insurance payments until year 2011.

6

cash-collateral requirements.

The rationale for the effects described above is straightforward. According to theoretical results

in Bolton et al. (2011), cash inventory policy involves a combination of a double-barrier policy (as

in Miller and Orr (1966)) and the continuous management of cash reserves in between the barriers

through adjustments in investment, leverage, asset sales, payout policy and hedging positions. When

the level of cash is low (as after the Katrina shock) and the marginal value of cash is high, firms

engage in asset sales, scale back investment, cut payouts to shareholders and more importantly, cut

hedging (specially under costly margin requirements). Moreover, Lins et al. (2010) find that less

than half of the total cash held by companies is held for non-operational purposes, amounting to

only 2% of the assets. Therefore, the 4% average drop in cash holdings as a result from the Katrina

shock represents a substantial enough variation in cash holdings to affect the extent to which firms

can sustain their derivative contracts.

2.1 Empirical Specification

We build on the following empirical specification for the extensive margin of hedging for firm i

located in state s in fiscal year t. Variable Hedgeist considers a hedging dummy for transactions

with derivatives that are not for speculative purposes.17

Hedgeist = δj ∗ γt + θi + ρhZist +X ′firmist βh + ϕist, (1)

where Zist = (Treatis ∗ Postt) is the source of exogenous variation in the identification strategy.

Treatis is a dummy variable that takes the value of 1 for firms in the treatment group, firms located

in Katrina states in the pre-treatment period, and 0 else. Postt is a dummy variable that takes the

value of 1 in the post-treatment period and 0 else. Xfirmist contains all observable firm characteristics

that are relevant for the hedging decision, including; leverage, tangibility, size, profitability, market-

to-book, collateral for debt and derivatives, a dummy variable identifying rated firms and a dummy

variable identifying dividend-paying firms (as in Disatnik et al. (2013) or Tufano (1998)).

θi, δj and γt are firm, industry and year fixed effects which control for unobserved heterogeneity

across firms, industries and time, respectively. We include higher-order fixed effects, δj ∗ γt, in the

form of industry-by-year fixed effects. Without δj ∗ γt, the specification allows to compare any

treated firm’s hedging and any control firm’s hedging within a given fiscal year t. However, hedging

is generally industry-specific (Rampini et al. (2014), Bakke et al. (2016), Tufano (1998)) and thus, we

17We borrow the hedging dummy from Biguri et al. (2018) who build a dummy variable capturing firm-year obser-vations using derivative contracts for hedging purpose according to Sfas. 133 “Accounting for Derivative Instrumentsand Hedging Acitivities”.

7

need to ensure that the comparison of treated and control firms remains within the same industry.18

Additionally, we cluster the standard errors at the state-level (the source of variation) to account

for potential serial correlation among firms within states as the shock affects all firms in a state at

the same time.

From equation (1) above, we are interested in the sign and the statistical and quantitative

significance of ρh. ρh measures the effect of hurricane Katrina on the extensive margin of hedging

for firms located in the Katrina states as compared to other firms located elsewhere in the U.S.

within a specific industry. We expect ρh < 0, implying that firms located in Katrina states reduce

hedging as a response to the liquidity shock.

However, testing equation (1) alone does not show that the reduction in hedging occurs because of

collateral requirements. As a result, we also study the response of collateral pledged for derivative

contracts as a result of the liquidity shock in a DID setup. CollDerist is the dummy variable

identifying firms that are pledging collateral for derivative transactions and we use the following

empirical specification for firm i located in state s in fiscal year t:

CollDerist = δj ∗ γt + θi + ρcdZist +X ′firmist βcd + ϕist. (2)

All the variables are defined analogously to those in equation (1). In this case, we are interested in

the sign and the statistical and quantitative significance of ρcd. We expect ρcd < 0, implying that

firms located in Katrina states are affected by the liquidity shock and reduce collateral pledged as

hedging requires cash-collateral. Therefore, a necessary condition to test our channel and mechanism

is to have both ρh and ρcd negative and significant.

Finally, we also analyze the response of capital expenditures standardized by total assets. Vari-

able Capexist analyzes the response of firm i located in state s in fiscal year t:

Capexist = δj ∗ γt + θi + ρcZkist +X ′firmist βc + ϕist. (3)

We are interested in the sign and the statistical and quantitative significance of ρc. We are unsure

about the sing of ρc in the context of our identification strategy. On one hand, we rule out from

the treatment group those firms that experienced property damage as a result from the shock and

thus, we should not observe an increase in capital expenditures from firms replacing lost or damaged

18Building on work by Gormley and Matsa (2013) we avoid controlling for state-by-year fixed effects to accountfor differences in local economic environments over time. Their work suggests that the estimator may suffer from ameasurement error bias because the sample mean of the group’s dependent variable may measure the true unobservedheterogeneity with error. Another reason that justifies the inclusion of industry-by-year fixed effects is the lack of dataon counterparties in the hedging relation. This is a potentially relevant omitted variable. The comparisson withinthe same industry alleviates this concern as specific industries are likely to use similar hedging instruments that havesimilar counterparties behind.

8

PPE. On the other hand, there is vast evidence showing how the investment and liquidity of severely

constrained firms reacts to negative liquidity shocks (Bolton et al. (2013) or Campello et al. (2010)).

3 Data

We start with all U.S. public firms covered by Compustat from 1996 to 2015. We remove financial

firms (SIC codes 6000-6999).19 We then merge the resulting sample of the Compustat firms with

our sample of collateral for derivatives and debt, which results in a final sample of 62,378 firm-

year observations. All continuous firm characteristic variables are winsorized at the 1st and 99th

percentile. Table A1 in the Appendix provides a detailed description of the variables used in our

analysis.

In order to construct the Katrina Sample, we drop all firm-year observations that do not belong

to the 2002-2005 fiscal years interval. We further discard those firm-year observations that have

an asset impairment in fiscal year 2005 when located in Katrina states. We define Treat1 as all

firm observations that are located in the Katrina States. Similarly, we define Treat2 as all firm

observations that are located in the states of Louisiana and Mississippi and firm observations in

Florida that are located in cities that are affected by hurricane Katrina.20

3.1 Collateralization in Derivative Transactions

Firms that engage in derivative transactions face a trade-off. On one hand, firms are able to

hedge the underlying risk exposures of their business. However, at the same time, they create

new risk exposures associated with the risk of the counterparty defaulting on payments. Generally,

counterparty credit risk exposure is first reduced through credit quality analysis and netting, and

the remaining net exposures are further reduced through collateralization.21 Figure 3 shows a list

of the available instruments to alleviate credit risk for non-financial corporations.22

During the early stages of the derivatives market, there was a tendency to merely deal with

the most creditworthy counterparties. However, the increasing use of collateral has enabled the

expansion of the market to include also less creditworthy counterparties. In the U.S., the typical

form of collateralization is the pledge of assets. That is, the creation of a security interest in favor

19Although utilities (SIC codes 4900-4949) are generally excluded because they may face other regulatory aspectsdifferent from other sectors, we keep them in the sample because of their extensive use of derivative instruments. Onaverage, 32% of firm-year observations in the utility sample hedge from 1996 to 2015, while 13% pledge collateral forderivative transactions.

20This includes Coral Gables, Pembroke Pines, Fort Lauderdale (and Ft. Lauderdale), Pompano Beach, Boca Raton,Deerfield Beach, Coral Springs, Lehigh Acres, Miami Beach, Oakland Park, Miami Lakes, Bonita Springs, BoyntonBeach, Wilton Manors, Delray Beach, Cooper City and Cape coral.

21Central to netting is the concept of a master agreement that governs transactions between counterparties. Multipletransactions can be reflected under the same master agreement forming a single legal contract of indefinite term. Asa result, all exposure from mark-to-market variations can be netted under the same master agreement.

22More information on collateralization and its evolution over time can be found in ISDA (1996), ISDA (1999), ISDA(2005) and ISDA (2013) for OTC derivatives, while CMEGroup provides information for exchange-traded derivatives.

9

of the collateral taker.23 Collateralization in the derivatives market works in a similar fashion as in

the debt market. However, there are subtle differences because of the mark-to-market nature of the

derivatives market. Figure 4 shows the two different collateral requirements present in derivative

transactions: the initial margin and the variation margin.24

In derivative transactions, the value of the trade can vary day by day and thus, a variation

margin can be required. The party which is “out-of-the-money” or for whom the current credit

exposure goes beyond some pre-specified threshold can be required to post collateral. This way each

counterparty is paid what it is owed even if the trade is terminated that day. On the other hand,

the initial margin is deposited at the inception of the derivative transaction and it is a buffer that

insulates the surviving party against further losses following a default. Therefore, collateral acts as

a backstop that protects market participants and the economy as a whole. In some cases, companies

do not have a collateral requirement (unsecured transaction). In others, they are obliged to pledge

collateral only if their credit quality falls beyond some credit rating threshold. These requirements

are generally called collateral credit triggers.

Finally, three simple rules to understand what parties pledge collateral for derivatives transac-

tions. First, low credit quality firms generally pledge collateral. Second, firms with derivatives in

a (net) liability position can pledge collateral. Third, exchange-traded derivatives have tradition-

ally had a mandatory requirement for collateral (both in terms of initial and variation margin and

haircuts were applied depending on the liquidity of the collateral pledged). Instead, OTC provided

end-users with more flexibility as bilateral arrangements allowed counterparties to decide on the

terms of the contracts, but also on whether and how to include collateral requirements and haircuts

on eligible collateral. Therefore, instruments that have traditionally traded in exchanges such as

standardized futures and options always have a collateral requirement, while instruments that are

generally traded OTC such as forwards do not. The financial crisis and the posterior regulatory

changes in the OTC market have introduced some changes in the practice of collateralization for

non-financial counterparties (NFC). While in the U.S., NFCs have been left outside the regulation,

the European authorities have imposed mandatory collateral requirements and central counterparty

clearing (CCP) to NFCs dealing with large trades.

23The securities are delivered to the collateral taker or custodian, while the collateral giver remains as the ownerof the securities. The other option is the title transfer, which basically allows the taker to own the collateral withoutrestriction.

24The initial margin is called the independent amount in OTC derivatives. See LoPucki et al. (2012), LoPucki(2003) and Bolton and Oehmke (2015) for further information on the priority structure of derivatives and corporateliabilities.

10

3.2 Manual Data Collection and Text-Search Algorithm

Our work focuses on the role of collateralization as a credit risk mitigation technique in derivative

transactions. We use manual data collection and a text-search algorithm to generate the required

data for the analysis that are not present in databases for U.S. public companies such as Compustat.

We create this data by searching through the annual reports (10-K filings) of all U.S. public firms.

These firms are required by law to file material information electronically with the Securities and

Exchange Commission (SEC) since 1996. The SEC handles the electronic filing through the Elec-

tronic Data Gathering, Analysis, and Retrieval system (EDGAR). The primary purpose of EDGAR

is to allow investors timely access to price relevant corporate information.

The text-search algorithm searches for specific keywords in all available 10-K, 10-KT, 10-K405,

10KSB, and 10KSB40 filed in the SEC’s EDGAR system. More precisely, we use the text-search

algorithm to generate the following variables:25

• Collateral for debt contracts: we generate dummy variables identifying firm-year observations

with i) property, plant and equipment, ii) intangible assets, iii) receivables, iv) inventories, v)

cash and marketable securities or vi) all assets pledged as collateral for financial debt contracts.

• Collateral for derivatives contracts: we generate dummy variables identifying firm-year obser-

vations with i) cash and marketable securities, ii) property, plant and equipment, iii) letters

of credit and vi) other sources of pledged or received as collateral for derivatives contracts.

• Collateral valuation: we then read the surrounding text and gather the valuation of the collat-

eral pledged/received for financial debt and derivatives contracts.26 In the case of derivative

transactions, disclosure requirements imply that gross total collateral is reported.

• Collateral credit trigger: we generate a dummy variable identifying firm-year observations with

a collateral credit trigger.27

The following paragraph is an example of the raw data from which the sources of collateral and

its valuation from debt contracts is obtained:

“AK Steel Holding Corporation announced today that its subsidiary, AK Steel Corpo-

ration, has successfully priced a private offering of $30.0 million aggregate principal

amount of its 8.750% senior secured notes due 2018, [...] fully and unconditionally

25Appendix A2 provides detailed information on how these text-search variables have been constructed.26All items are gathered at market value except for the valuation of PPE for financial debt contracts, which is

disclosed at net book value.27Appendix A3 discusses the disclosure requirements for notional amounts and collateral requirements further.

11

guaranteed on a senior basis by AK Holding and will be secured by substantially

all real property, plant and equipment of AK Steel and proceeds thereof.”

The following paragraph is an example of the raw data from which the sources of collateral and its

valuation from derivative contracts is obtained:

“We minimize the credit risk exposure by limiting the counterparties to those major banks

and financial institutions that meet established credit guidelines. [...] To further

mitigate the risk of counterparty default, we maintain collateral agreements with

certain counterparties. The agreements require both parties to maintain cash deposits

in the event the fair values of the derivative financial instruments meet established

thresholds. We have placed cash deposits totaling $206 million and $125 million at

December 31, 2006 and 2005, respectively, in accounts maintained by counterparties.

We have received cash deposits from counterparties totaling $215 million and $247

million at December 31, 2006 and 2005, respectively.”28

The following excerpt shows an example of a collateral credit trigger gathered from Kellogs’ 2009

10-K filing:

“Certain of the company’s derivative instruments contain provisions requiring the

company to post collateral on those derivative instruments that are in

a liability position if the company’s credit rating falls below BB (S&P),

or Baa1 (Moodys). The fair value of all derivative instruments with credit-risk-

related contingent features in a liability position on January 2, 2010 was $18 million.

If the credit-risk-related contingent features were triggered as of January 2, 2010, the

Company would be required to post collateral of $18 million.”

Table 1 presents descriptive statistics for all U.S. public firms from 1996 to 2015. The average

Compustat firm has 22.1% book leverage, invests 5.6% of total assets, holds 21.5% of cash and

marketable securities over total assets, and 29.8% of the firm-year observations hedge. As regards the

prevalence of collateral requirements, 55.8% of firm-year observations have a collateral requirement

for debt contracts, while only 1% of them have a collateral requirement for derivatives. Note

that given the 29.8% of the firm-year observations being hedgers, preliminary results suggest that

collateral requirements for derivatives are not the rule but rather the exception. Table A4 in the

Appendix reports summary statistics for collateral requirements on derivatives and financial debt

for the Rampini et al. (2014) sample. We have manually gathered their sample from the annual

28This excerpt summarizes the pecking-order previously described in section 3.1 and Figure 3 for credit risk mitiga-tion techniques. First, credit quality and then, collateralization.

12

reports in the SEC. The main conclusions are consistent with the whole sample analysis and Figure

5 summarizes them.

For further suggestive evidence on collateral usage of U.S. public corporations, Figure 6 presents

the time series evolution of the extensive margin for hedging and collateral requirements for deriva-

tives from 1996 to 2015. The left panel shows the time series for the extensive margin for collateral

requirements for derivatives, while the right panel shows the evolution of the intensive margin. The

graphs illustrates a remarkable increase of risk management practices for U.S. public firms over time

as well as in collateralization requirements in derivative transactions.29 Survey evidence also con-

firms a higher degree of awareness by non-financial corporations of risk management and its benefits

(and costs).30

The analysis of summary statistics and figures does not provide evidence in support of the

financing versus risk management trade-off. Thus, we next turn to the investigation of the effects

of collateral requirements on hedging and corporate policy decisions. The following section presents

the results of the remaining empirical analysis.

4 Results

In this section, first, we analyze the interaction between financial constraints, collateral requirements

and corporate policy decisions. We then focus on whether and how firms can render constrained

due to collateral requirements and how this eventually impacts liquidity, risk management and

investment decisions. We look at two different experimental setups that allow us to exploit exogenous

variation in terms of cash- and PPE-collateral requirements (in the Robustness Checks section) and

observe the reaction of hedging. Finally, we analyze the implications of our results in the context

of recent regulation in OTC derivatives as a result from the 2007 financial crisis.

4.1 Financial Constraints, Collateral Requirements and Corporate Policy Deci-

sions

First, we investigate the extent to which financially constrained firms engage less in risk manage-

ment activities using derivative instruments for hedging purposes. Table 2 reports ordinary least

squares (OLS) estimation results for cash holdings, hedging, investment and leverage when consid-

ering financial constraints definition in Almeida et al. (2004) and Hadlock and Pierce (2010) (FC),

collateral requirements for financial debt (CollDebt) and their interaction term (CollDebt ∗ FC).

29This evolution is consistent with that reported by ISDA (2005)’s Collateral Guidelines. According to ISDA reportsand information gathered in private conversations with the CMEGroup, collateralization started to develop in thebeginning of the 90’s. Another driver of the increase in hedging is that 10-15 years ago, exchanges like the CMEGroupdid not pay interest on cash pledged as collateral (yield). This was not beneficial for multinational corporations todecide to use derivatives.

30See Giambona et al. (2018) and Servaes et al. (2009) for the most recent survey evidence on risk management andBodnar et al. (1998) for evidence from the late 90’s.

13

Our results suggest that firms with a collateral requirement that are financially constrained, hedge

less on average (in the range of 5-13%). These firms also have lower cash holdings (1-2%) and higher

leverage (around 2%). When firms with a collateral requirement for financial debt switch from the

unconstrained to the constrained status, while cash holdings and leverage remain similar, hedging

is the only corporate policy decision being adjusted. Therefore, these results are consistent with

evidence in Rampini et al. (2014) and Nance et al. (1993).

However, if we focus on the results for firms with a collateral requirement only, we can see that

collateral requirements for financial debt do not seem to be the reason why firms hedge less or do

not hedge at all. These firms exhibit hedging rates between 15-26% higher, while they can still

sustain lower (higher) than average cash holdings (leverage). We observe no statistically significant

results in terms of investment.

In order to understand the relation between collateral requirements and financial constraints

better, Figure 7 shows a local polynomial approximation for the extensive margin of collateral

requirements for debt (left panel) and the intensive margin of collateral requirements for derivatives

(right panel) as a function of the S&P’s long-term credit rating. The relation between credit quality

and collateral requirements for debt contracts is negative, but non-linear. Firms with higher credit

quality rely on unsecured debt financing instead and thus, tend to have no collateral requirement

(Rauh and Sufi (2010)). When focusing on the intensive margin for collateral requirements we

see that the main collateral absorption for financial debt is located between BBB- and B- (at the

threshold for investment grade).

On the other hand, the right panel in Figure 7 shows the relation between credit quality and

collateral requirements for derivatives. As credit quality increases, firms pledge less collateral in

derivatives contracts.31 Finally, Figure 8 shows the relation between S&P’s long-term credit rating

and the existence of collateral credit triggers in derivative transactions for the firm (left panel) and

the time series evolution of the series (right panel). The left panel suggests that the likelihood

of having collateral triggers increases as the credit quality decreases. The prevalence of collateral

credit triggers peaks at BBB+ (investment grade threshold) and then, slowly reduces back to zero

at CCC-. The time series evolution also shows an interesting pattern. We observe a sharp increase

in the prevalence of collateral triggers for firms in the sample around the 2007 financial crisis.

Consequently, the likelihood of collateralization for derivatives increased from 1 to 3% during that

same period.

Next, we focus on firms’ characteristics and corporate policy decisions when they face a collat-

31If we instead look at the relation between collateral requirements for derivatives and book or market leverage, wefind a clear positive relation between leverage and collateral requirements for derivatives at the extensive and intensivemargins.

14

eral requirement for financing or risk management purposes. Table 3 reports summary statistics,

mean and standard deviation, for characteristics of firms having different collateral requirements; i)

firms with a collateral requirement for financial debt only, ii) firms with a collateral requirement for

derivatives only, iii) firms with a collateral credit trigger for derivatives and iv) firms with a collat-

eral requirement for financial debt and derivatives contracts. Several conclusions can be highlighted.

First, firms pledging collateral for financial debt contracts are smaller, have lower investment op-

portunities, leverage, profitability, tangibility, unsecured debt over total debt, investment and R&D

investment, while they have higher cash holdings and acquisitions as compared to firms pledging

collateral for derivative transactions. Moreover, firms pledging collateral for debt contracts seem to

be more financially constrained according to definitions in Almeida et al. (2004) and Hadlock and

Pierce (2010).

Second, firms pledging collateral for derivatives or with a collateral credit trigger for derivatives

have the lowest average cash and marketable securities holdings; 9.6% and 4.2%, respectively. They

also have the highest average investment; 6.7% and 7.1%, respectively. These results suggests that

hedging reduces the need to hold that much precautionary cash as previously suggested in the lit-

erature (Disatnik et al. (2013), Almeida et al. (2014)), while still preserving investment capacity.

However, as compared to those firms hedging with a collateral credit trigger, firms pledging collat-

eral for derivatives need higher holdings of cash and marketable securities presumably because of

cash-collateral requirements. Therefore, for collateral-pledging hedgers, a complementary relation

between liquidity and risk management arises (Bolton et al. (2011)).

We next focus on corporate policy decisions exclusively. Table 4 reports summary statistics, mean

and standard deviation, for the different corporate policy decisions sorted by collateral requirements

for financial debt contracts and collateral requirements for derivatives. We analyze the role of cash

holdings, acquisitions, dividend payments, R&D investment and capital expenditures.

Firms with the highest average cash holdings are those without collateral requirements (15.5%),

while the lowest correspond to those with a collateral requirement for derivatives only (7%). If

we focus on those firms with a collateral requirement for derivatives, we observe that additionally

having a collateral requirement for financial debt increases average cash holdings by 1%. That is,

firms with both collateral requirements tend to hold higher cash holdings. Then, we focus on how

acquisitions, R&D investment and dividend payments are affected by collateral requirements. We

observe that firms with the highest average acquisitions and R&D investment are those without

collateral requirements (3.1% and 73%), while dividend payments seem to be more popular among

firms with a collateral requirement for derivatives only. Finally, in terms of capital expenditures,

firms with the highest investment are those with a collateral requirement for derivatives only, while

15

the lowest corresponds to those with a collateral requirement for financial debt only.

Overall, the results in this sections yield two main implications. First, collateral requirements

for financial debt contracts do not seems to be behind the lack of hedging for financially constrained

firms. Second, the suggestive evidence on corporate policy decisions shows that risk management

reduces the need to hold precautionary cash but only to the extent that collateral is not required to

secure derivative transactions. When firms are obliged to pledge cash-collateral, the relation turns

into a complementarity relation.

4.2 Corporate Policy Responses to a Liquidity Shock

In this section we deepen on the interaction between hedging, cash holdings and investment in

the context of collateral requirements, and analyze the effect of a shock that brings cash holdings

temporarily out of the optimal level. We look at the effect of hurricane Katrina in 2005 for firms

located in states that were in the path of the hurricane in terms of firms’ risk management decisions.

Figure 2 shows the pre- and post-treatment average cash holdings for the treatment and control

groups. Consistent with the Katrina liquidity shock assumption, the figure shows a 4% decrease in

average cash holdings for firms in the treatment group.

Table 5 reports regression output for the DID identification strategy based on the effect of hur-

ricane Katrina in 2005. Columns (1)-(2) show the average treatment effect (ATE) on the extensive

margin of hedging for post-treatment period 2005 for treatment group 1 (Treat1).32 The results

are negative and statistically significant. Firms in Katrina states reduced hedging by more than

similar firms in non-Katrina states as a result from the liquidity shock. More precisely, an average

4% decrease in cash holdings leads to a reduction of hedging of 1.5-1.8%. The results are consistent

with the idea that cash plays a dual role: it is a liquidity management instrument but also the

main source of collateral to alleviate counterparty risk for derivative transactions. The results also

suggest that cash holdings and hedging have a complementary relation which adds to existing work

by Disatnik et al. (2013). We complement their results by showing that because firms financially

constrained can access the derivatives market by pledging cash-collateral, liquidity and risk man-

agement decisions are inevitably linked (Keynes (1936)). Finally, the main take away from these

results is that cash-collateral constraints are binding for risk management.

We also look at the response of investment for firms in the treatment group due to the Katrina

liquidity shock. Columns (5)-(6) show the ATE on capital expenditures over total assets for post-

treatment period 2005 and Katrina states (Treat1). The results are not statistically significant. The

liquidity shock impacts risk management decisions of firms while leaving investment unaffected.

As a robustness check, we also look at the response of collateral pledged for derivatives. To the

32See Section 2 for the definitions of treatment and control groups.

16

extent that hurricane Katrina leads to lower hedging via cash-collateral constraints, we should also

observe a decrease in collateral pledged for firms in the treatment group. Columns (9)-(10) show the

ATE on collateral requirements for post-treatment period 2005 and firms located in Katrina states.

The results are negative and statistically significant. Firms in Katrina states reduced collateral

pledged by 0.1% more than similar firms in non-Katrina states as a result from the liquidity shock.

We also use a more precise assignment of treatment. Hurricane Katrina only impacted the south

of Florida and thus, assigning firms located in the north of Florida may lead to inaccurate estimates

of the effect of treatment. Therefore, for robustness, we build a second definition of treatment

that assigns only firms located in cities in FL that were affected by Katrina along with all firms

located in the states of LA and MS, treatment group 2 (Treat2). Columns (3)-(4), (7)-(8) and (11)-

(12) report the estimated coefficients for the hedging dummy, capital expenditures over total assets

and collateral pledged for derivatives, respectively. Consistent with the more precise assignment of

treatment, estimated coefficient increase in magnitude. More precisely, 1.8% versus 2.0% and 0.07%

versus 0.14% for hedging and collateral for derivatives, respectively. Overall, the results from the

analysis in Table 5 suggests that cash-collateral constraints are binding for risk management, while

investment remains unaffected when firms are hit by a temporary liquidity shock.

While treating derivative transactions as homogenous has the advantage of simplicity, we also

analyze the DID response of different types of derivatives, which may also have different collateral

requirements. More precisely, some risk management strategies may require a cash outflow (i.e.

long position on put option), while other strategies like linear strategies or some collars or forward

contracts do not. Table 6 shows our estimation results for the extensive margin of derivative instru-

ments by exposure and type, including; foreign exchange, interest rate, commodity prices, futures,

options, forwards, swaps and other instruments (i.e. collars, caps, floors,...) as a result from the

Katrina liquidity shock. Our results suggest that the reduction in hedging we observe comes from

firms located in the Katrina states relying less on foreign exchange (by exposure) and options and

futures (by type of instrument) derivatives. Our results are aligned with the mandatory collateral

requirements in terms of initial and variation margin for exchange-traded derivatives. Moreover,

we do not observe a reduction in those instruments that are generally transacted in OTC deriva-

tive markets such as forwards. These results provide further suggestive evidence on cash-collateral

constraints being the mechanism behind the reduction in hedging.

A final remark is needed. Note that in our analysis, the liquidity shock leads to terminating

hedging contracts. We are not able to observe the intensive margin of hedging (i.e. the notional

amount or the fair value of derivatives standardized by total assets) as firms do not report this

information in the period 2002-05 in the their annual reports. It is an important limitation of this

17

study. However, most likely, some firms will not terminate hedging but will adjust the size of the

contracts for the exposures being hedged. Moreover, the extent to which investment is affected by the

liquidity shock will depend on the size of the liquidity shock, the existing investment opportunities

and the pecking-order for the different uses of cash (e.g. financing capital expenditures, acquisitions,

dividends and share repurchases, ineffective hedges or collateralization for instance).

In unreported results, we also test some of the theoretical predictions in Bolton et al. (2011). We

look at the reaction of R&D-to-sales, acquisitions over total assets, access to a credit line, common

and total dividends over total assets, share repurchases over total assets and book leverage in the

context of the Katrina identification strategy. Our results suggest that the negative liquidity shock

also affects other corporate policy decisions. Consistent with Bolton et al. (2011) we find a reduction

in dividends and a lower likelihood of using a credit line after the shock. However, we also observe

an increase in book leverage for those affected by the liquidity shock. Finally, we also test whether

hedge effectiveness plays a role in the context of collateralization and the Katrina identification

(Gilje and Taillard (2017), Disatnik et al. (2013)). We look at the reaction of effective hedges (i.e.

cashflow hedges, fair value hedges and net investment hedges) and ineffective hedges according to

Sfas. 133. We learn that the decrease in hedging we observe is motivated by a reduction in ineffective

hedges. This is consistent with ineffective hedges creating additional idiosyncratic risk exposures to

the firm and thus, require additional cash for precautionary reasons. Moreover, we also observe a

decrease in fair value hedges.

4.3 Implications for Recent Regulatory Changes in the OTC Market

The results derived in this section have important implications for recent changes in the regulation of

the OTC derivatives market in the post-crisis reform. The 2007 global financial crisis was propagated

and amplified due to counterparty exposures related to the OTC market, as many exposures were

not collateralized.33 In September 2009, G-20 leaders agreed on the main rule to increase the

transparency and reduce systemic risk in the OTC markets.34 In 2011, the G20 agreed to add

margin requirements on non-centrally cleared derivatives to the reform programme (BIS (2013b)).

Non-financial counterparties (NFC) represent a large share of the total number of counterparties

in the OTC market (around in 72%), while the share over total volume is low (around 7%). The new

regulation had a different impact on non-financial corporations in the U.S. and in Europe. Reforms

in the U.S. were carried out under the Dodd-Frank Wall Street Reform and Consumer Protection

33Estimations by the Macroeconomic Assessment Group on Derivatives suggest that the cost of systemic crises canbe about 60% of GDP and thus, the expected value of the benefit from new regulation was estimated at 0.16% ofGDP.

34Changes included that standardized OTC derivatives should be traded on exchanges or electronic platforms andsettlement should be done through central counterparties (CCP), the registration of OTC derivatives trade repositoriesand a higher capital requirement for non-CCP OTC derivatives.

18

Act and excluded NFCs from the requirement to CCP and the provision of mandatory margin. In

Europe, the European Market Infrastructure Regulation (EMIR) decided to ease the burden for

small financial counterparties (SFCs) only. SFC were released from the clearing obligation when the

volume of OTC derivatives they dealt with was too low to represent an important source of systemic

risk to the financial system and too low for CCP to be economically viable.

We think our work relates to recent discussion on the regulatory aspects of OTC derivatives in

two manners. First, we argue that the results reported for the Katrina identification strategy analyze

the plausible implications of the extension of the CCP requirement and the mandatory initial and

variation margin provision to all non-financial corporations in the U.S. and Europe. More precisely,

higher demand for collateral, a more stringent pool of eligible collateral and/or higher haircuts

applied to the pool of eligible collateral would generate similar implications to those evaluated in

the shock to liquidity Katrina identification strategy. It would greatly increase the operational

complexity of hedging through derivative instruments. As a result, firms at the margin would

experience a reduction in their willingness to engage in derivative transactions as it would become

too costly. Giambona et al. (2018) provide similar suggestive evidence in their risk management

survey.35

Second, it is important to clarify the direct and indirect effects of changes in OTC regulation

for non-financial corporations. The scant literature on the the matter is divided. Mello and Parsons

(2013) argue that margin requirements for non-financial corporations do not alter firms willingness to

engage in hedging, while Silva-Araujo and Leao (2016) show that the higher cost of OTC derivatives

was transferred to the non-financial sector for a sample of Brazilian firms. Visual inspection of the

right panel in Figure 6 provides further insight on presumably, the effects of OTC regulation for non-

financial corporations. The graph plots the time series for the hedging dummy and collateral pledged

in derivative transactions standardized by total assets. Two interesting facts can be highlighted.

First, we observe a spike in collateral requirements for derivative contracts at the time of the collapse

of Long-Term Capital Management L.P. in 1998. The second spike coincides with the implementation

of OTC derivatives reform and may suggest both direct and indirect effect passed by counterparties

to non-financial corporations. Further research on these issues is needed to clarify the effects of the

regulation on the real economy.

35In Table 10, in panels A and B respectively, non-financial institutions responding the survey disclose that 66%would not change the usage if regulation changed toward all centrally cleared (exchange-traded) derivatives, while 53%would decrease usage if regulation changed toward higher cash-collateral requirements or increased collateral positions.

19

5 Robustness Checks

In this section, we undertake several robustness checks. First, we look at the effect of a shock to

PPE on firms’ risk management decisions building on the identification setup in Chaney et al. (2012)

on the collateral channel for investment. Second, we use a propensity score matching technique in

the Katrina identification strategy to avoid the possibility of a selection bias in our observed average

response of hedging. Third, we perform a placebo test on the Katrina identification strategy by

artificially moving the date of the hurricane to 2002. Finally, we build on Opler et al. (1999) and

the recent paper by Graham and Leary (2018) and analyze whether collateral requirements for

derivatives affect the level of cash firms decide to hold.

5.1 Risk Management’s response to a PPE Shock

From the previous analysis, we have concluded that cash-collateral constraints are binding for risk

management and that PPE seems to play a residual role in collateralization for derivative trans-

actions. This is the case as the mark-to-market nature of derivatives calls for more liquid source

of collateral to reduce counterparty credit risk. Still, one unexplored possibility is that firms use

their PPE to satisfy margin calls indirectly. That is, when a firm receives the margin call or at the

inception of the derivative transaction when it is required to pledge an initial margin, firms could

go to their relationship bank and use the PPE to secure a debt contract that provides the necessary

cash to satisfy the margin requirement. If this were the case, then, we could still observe a trade-off

between risk management and investment because of collateral requirements.

Therefore, next we test the effect of a shock to PPE collateral on the extensive margin of hedging.

We build on the identification setup in Chaney et al. (2012) and exploit local variations in local real

estate prices, either at the state or the city level, as shocks to the collateral value of land holding

firms. We address the endogeneity of local real estate prices using two sources of identification.36

The first comes from the comparison, within a local area, of the sensitivity of hedging to real estate

prices across firms with and without real estate. The second comes from the comparison of hedging

by land holding firms across areas with different variations in real estate prices.37

We measure how a firm i located in state s’s corporate hedging in year t (Hedgesit) responds to

each additional dollar of real estate that the firm actually owns (RE Valuei), and not how hedging

36Real estate prices may be correlated with the investment opportunities of land-holding firms, and also, the decisionto own real estate may be correlated with the firm’s investment opportunities.

37The methodology is similar to Case et al. (2001) in their study of home wealth effects on household consumption.

20

responds to real estate shocks overall.38

Hedgesit = γt + θi + βRE ValueiP st

P s93

+ δP st

P s93

+ κ∑k

XikP

st + controls

′itα+ εit, (4)

where RE Valuei is the market value of real estate assets in 1993 to lagged PPE,P st

P s93

measures the

growth in real estate prices in state s from 1993 to year t, Xik are controls that play an important

role in the ownership decision and include 5 quintiles of age, assets, profitability and leverage, 2-

digit industry and state dummies interacted with the level of real estate prices, controls′it control

for the ratio of cashflows to PPE, market-to-book value of assets. θi and γt control for unobserved

heterogeneity across firms and over the business cycle. Errors are clustered at the source of variation;

at a state-firm level.39

We are interested in the sign, statistical and economic significance of β, which measures how

a firm’s hedging responds to each additional unit of real estate the firm actually owns. Table

7 reports the empirical link between the value of real estate assets and the extensive margin of

hedging. Column (1) uses the state-level residential price index, while Columns (3) uses Metropolitan

Statistical Area (MSA)-level residential prices. We find a positive but statistically not significant

estimated coefficient for the two regression specifications included in Columns (1) and (2). Real

estate price variation does not seem to affect risk management decisions of firms. These results

are not consistent with a trade-off between risk management and investment because of collateral

requirements.

We also re-estimate equation (4) for collateral for derivatives in Columns (3) and (4) and capital

expenditures standardized by lagged property, plant and equipment in Columns (5) and (6). The

result on collateral for derivatives are negative but statistically not significant. This means that

variation in real estate prices does not have an impact on the likelihood of a company to pledge

collateral for derivative transactions. Finally, the results for investment yield the same conclusion

as in Chaney et al. (2012). They show a positive and statistically significant coefficient for real

estate prices both using the state-level residential price variation and the MSA-level residential

price variation.

5.2 Katrina Identification with Propensity Score Matching

The analysis in Tables 5 and 6 suggests that the liquidity shock caused by hurricane Katrina reduced

hedging by firms due to collateral requirements. However, our observed difference in average hedging

38We look at the period from 1996 to 2007, as opposed to Chaney et al. (2012) which start in 1993. We do thisbecause the data from EDGAR is only available from 1996 on.

39For completeness, we also look at the reaction of collateral for derivatives and investment using the same empiricalspecification as in equation (4).

21

and collateral pledged in derivatives could be subject to a selection bias (Angrist and Pischke

(2009)). One way to address concerns regarding whether treatment is as good as randomly assigned

is to investigate whether the conditional independence assumption is satisfied. That is, to test

whether relevant firm characteristics are statistically significantly similar for the treatment and the

control group in the pre-treatment period. We report this comparison in Table A5 in the Appendix.

Unfortunately, neither firm characteristics under Treat1 nor Treat2 are similar to the ones reported

in the control group.

Rosenbaum and Rubin (1985) and Smith and Todd (2005) propose propensity score matching

as a method to reduce the selection bias. In order to equate the observed difference in average

hedging and collateral pledged to the ATE on the treated, we use a matching approach to improve

the resemblance of firms receiving treatment versus those they do not.40 Our data is well suited for

the use of the matching procedure as the pool of controls (firms not located in Katrina states) is

particularly large and thus, it is more likely that we can find candidates in the control group that

match the firm-observations in the treatment group. We match treatment and control groups along

the size, book leverage, profitability, market-to-book, tangibility, rated dummy, dummy for dividend

payers and collateral for debt and derivatives dimensions.

Table 8 reports the results for the main variables analyzed in Tables 5 and 6 in Panel A) for

treatment group 1 (Treat1) and in Panel B) for treatment group 2 (Treat2). The main conclusion

derived in Tables 5 and 6 still hold after imposing that firm characteristics for treatment and control

groups need to be similar. However, the magnitude of estimated coefficients for the extensive margin

of hedging increases significantly. The estimated coefficient for the unmatched sample was equivalent

to 1.8% and 2% for treatment groups 1 and 2, respectively. The matching procedure, however, leads

to a further larger drop in the extensive margin of hedging equivalent to 7.0% and 8.1% for treatment

groups 1 and 2, respectively. The rest of the variables analyzed, including collateral for derivatives

yield very similar results as compared to those derived with the unmatched samples.

5.3 Katrina Identification Placebo Test

In order to make sure there is nothing in the way our identification strategy is defined that generates

a mechanical effect on firms’ corporate hedging decisions or their collateral requirements for deriva-

tives, we perform a placebo test with the Katrina identification strategy. We define treatment and

control groups in an analogous way as in the results section. However, we change the date when the

hurricane hypothetically hits Katrina states. We set the date of Katrina in fiscal year 2002 instead.

Table 9 reports regression output for the DID identification strategy based on the effect of

40We follow the exact matching procedure as in Michaely and Roberts (2012) and thus, their discussion of theintuition of the matching procedure in Appendix B can be used in our context too.

22

hurricane Katrina, but assuming the hurricane hits the treatment group in fiscal year 2002. Column

(1) shows the ATE on cash holdings, Column (2) shows the ATE on the extensive margin of hedging,

Column (3) shows the ATE on the extensive margin of collateral for derivatives and Column (4)

shows the ATE on investment. The results are statistically not significant for any of the variables

analyzed. Firms in Katrina states were not affected in terms of their hedging, liquidity management

or investment policies as compared to similar firms in non-Katrina states in fiscal year 2002.

5.4 Collateral Requirements in Derivatives versus the Level of Cash

Throughout the paper we have presented suggestive evidence that leads us to conclude that cash-

collateral requirements in risk management matter. However, one underlying assumption we have

made is that collateral requirements do indeed alter the level of cash firms hold. As a result, when

firms are hit by the liquidity shock they are forced to re-optimize the uses or purposes of their cash

and some firms at the margin, decide to cut hedging because of the cash-collateral requirements

they imply.

Nevertheless, if we look at the summary statistics for the whole sample in Table 1, we observe

that the pledge of collateral for derivatives is the exception rather than the rule. Moreover, we can

also observe that the intensive margin of collateral pledged for derivatives is relatively low (1%).

Therefore, do collateral requirements for derivatives really affect the amount of cash firms decide to

hold?

We build on Opler et al. (1999) and the recent paper by Graham and Leary (2018) and analyze

whether and how collateral requirements for derivatives affect the level of cash firms decide to hold.

In addition to all the firm-level controls used in Table 6 in Opler et al. (1999), we also include the

variables capturing collateral requirements for financial debt and derivatives that we gather in this

paper.41 Table 10 reports the results of the OLS estimation on the determinants of cash. We use

three alternative definitions for collateral in derivative transactions: the extensive margin (dummy)

in Columns (1)-(2), cash collateral over total assets in Columns (3)-(4) and total collateral over total

assets in Columns (5)-(6). We also replicate the analysis using the volatility of industry cashflows

(σ(Ind)) and the volatility of the quarterly cashflows of the firm (σ(CF )) as controls. One main

conclusion can be derived from the analysis performed. Collateral requirements for derivatives alter

the level of cash holdings firms decide to hold, regardless of the definition of collateral for derivatives

used or the control for the risk exposure of the firm used.

If we focus in Columns (1) and (2), we see that firms with a collateral requirement for derivatives

41We run the following empirical specification for cash over total assets:

Cashit = γt + θi + βderCollDerit + βdebtCollDebtit +X ′oplerit βopler + ϕit.

23

on average hold 1.7% less cash. This is consistent with evidence reported in Tables 3 and 4. Hedging

firms tend to hold less cash for precautionary reasons. Now, if we focus in Columns (3) and (4),

we can see how cash holdings vary with cash-collateral pledged over total assets. According to

the results, a 1 unit increase in cash-collateral requirements reduces cash holdings 0.25%. These

results are highly statistically significant. Moreover, they are economically relevant. A one standard

deviation increase in cash-collateral requirements leads to a decrease in cash holdings of 0.02 standard

deviation units or 0.3%.42

These results can add to the discussion on the dramatic rise in U.S. corporate cash holdings.

Under imperfect capital markets, holding cash allows firms to invest in positive net present value

project without delay. This precautionary savings story was due to ease of firms’ access to the

capital markets (Opler et al. (1999)) in the past, but recent work has focused on the role of in-

creasing investment uncertainty, foreign taxes and trapped cash as plausible explanations for firms

holding additional cash (Graham and Leary (2018)). Our results may suggest another reason that

complements these theories: the development of the derivatives market and the increasing use of

collateral for derivative transactions.

6 Conclusions

In this paper, we construct a novel database on collateral pledged/received in derivative transactions

and financial debt from 1996 to 2015 for all U.S. public firms. Despite the rapid growth in the use

of collateralization in the derivatives market in the recent years due to the 2007 financial crisis,

research on collateralization in risk management for non-financial corporations remains scant. To

the best of our knowledge, no paper has explicitly analyzed the sources and valuation of collateral

in derivative transactions (and financial debt) for non-financial corporations and whether and how

they affect corporate policy decisions. This paper aims to fill this gap in the literature.

We derive two main results. First, we show that cash and marketable securities are the main

source of collateral pledged for derivative transactions. Moreover, we show that when a negative

liquidity shock hits the firms and cash holdings are temporarily out of the optimal level, firms reduce

their risk management practices (extensive margin). We conclude that this result is driven by the

duality of cash as a liquidity management instrument and the main source of collateral to alleviate

counterparty risk in derivative transactions. Second, we also shed light on the complementary

relation between cash and hedging. To the extent that cash can be used to secure both derivative

transactions and debt contracts, liquidity tensions may arise. Moreover, we show that there is no

trade-off between risk management and investment because of collateral requirements. Overall, this

42We also look at the interaction term of collateral requirements for financial debt and derivatives, but it yields nostatistically significant results.

24

paper opens a new line of research on collateralization and its effect on corporate policy and the

effects of OTC regulation on risk management practices of non-financial corporations.

References

Acharya, Viral, and Alberto Bisin (2014) ‘Counterparty risk externality: Centralized versus over-

the-counter markets.’ Journal of Economic Theory, 149: 153-182

Almeida, Heitor, Murillo Campello, and Michael S. Weisbach (2004) ‘The cashflow sensitivity of

cash.’ Journal of Finance, 59(4): 1777-1804

Almeida, Heitor, Murillo Campello, Igor Cunha, and Michael S. Weisbach (2014) ‘Corporate liquid-

ity management: A conceptual framework and survey.’ Annual Review of Financial Economics,

6: 135-162

Angrist, Joshua D., and Jorn-Steffen Pischke (2009) Mostly Harmless Econometrics: An Empiricist’s

Companion (Princeton University Press)

Bakke, Tor Erik, Hamed Mahmudia, Chitru S Fernando, and Jesus M Salas (2016) ‘The causal effect

of option pay on corporate risk management.’ Journal of Financial Economics, 120(3): 623-643

Bates, Thomas W., Kathleen M. Kahle, and Rene M. Stulz (2009) ‘Why do U.S. firms hold so much

more cash than they used to?’ Journal of Finance 64, 1985-2021

Biguri, Kizkitza, Christian T. Brownlees, and Filippo Ippolito (2018) ‘Corporate hedging and the

variance of stock returns.’ Working paper

BIS (2013b) ‘Margin requirements for non-centrally cleared derivatives.’ Bank of International Set-

tlements

(2014) ‘Regulatory reform of over-the-counter derivatives: An assessment of incentives to clear

centrally.’ Bank of International Settlements

Bliss, Robert R., and George G. Kaufman (2006) ‘Derivatives and systemic risk: Netting, collateral,

and closeout.’ Journal of Financial Stability, 2(1), 55-70

Bodnar, Gordon M., Richard C. Marston, and Greg Hayt (1998) ‘Survey of financial risk manage-

ment by U.S. non-financial firms.’ Financial Management, 27(4): 104-114

Bolton, Patrick, and Martin Oehmke (2015) ‘Should derivatives be privileged in bankruptcy?’ Jour-

nal of finance: 70(6): 2353-2394

25

Bolton, Patrick, Hui Chen, and Neng Wang (2011) ‘A unified theory of Tobin’s q, corporate invest-

ment, financing, and risk management.’ Journal of Finance, 66(5): 1545-78

(2013) ‘Market timing, investment, and risk management.’ Journal of Financial Economics, 109:

40-62

Campello, Murillo, Erasmo Giambona, John R. Graham, and Campbell R. Harvey (2010) ‘Liquidity

management and corporate investment during a financial crisis.’ Review of Financial Studies 24,

1944-1979

Carter, D.A., D.A Rogers, and B.J. Simkins (2006a) ‘Does hedging affect firm value? Evidence from

the U.S. airline industry.’ Financial Management 35: 5386

Case, Karl E., Robert J. Shiller, and John M. Quigley (2001) ‘Comparing wealth effects: The stock

market versus the housing market.’ National Bureau of Economic Research Working Paper 8606

Chaney, Thomas, David Sraer, and David Thesmar (2012) ‘The collateral channel: How real estate

shocks affect corporate investment.’ American Economic Review, 102(6): 2381-2409

Cornaggia, Jess. (2013) ‘Does risk management matter? Evidence from the U.S. agricultural indus-

try.’ Journal of Financial Economics 109(2): 419-440

Degryse, Hans, Vasso Ioannidou, Jose Liberti, and M. Jason Sturgess (2014) ‘When do laws and

institutions affect the recovery rate of collateral?’ CEPR Discussion Paper No. DP11406

Dessaint, Olivier, and Adrien Matray (2017) ‘Do managers overreact to salient risks? Evidence from

hurricane strikes.’ Journal of Financial Economics, 126(1): 97-121

Disatnik, David, Ran Duchin, and Breno Schmidt (2013) ‘Cash flow hedging and liquidity choices.’

Review of Finance, 18(2): 715-748

Froot, Kenneth A., David S. Scharfstein, and Jeremy C. Stein (1993) ‘Risk management: Coordi-

nating corporate investment and financing policies.’ Journal of Finance 48(5): 1629-1658

Giambona, Erasmo, John R. Graham, Campbell R. Harvey, and Gordon M. Bodnar We (2018)

‘The theory and practice of corporate risk management: Evidence from the field.’ Financial

Management, 47(4): 783-832

Gilje, Erik P, and Jerome P. Taillard (2017) ‘Does hedging affect firm value? Evidence from a

natural experiment.’ Review of Financial Studies, 30(12): 4083-4132

26

Gormley, Todd A., and David A. Matsa (2013) ‘Common errors: How to (and not to) control for

unobserved heterogeneity.’ Review of Financial Studies, 27(2): 617-661

Graham, John R., and Mark T. Leary (2018) ‘The evolution of corporate cash.’ Review of Financial

Studies, 31(11): 4288-4344

Hadlock, Charles J., and Joshua R. Pierce (2010) ‘New evidence on measuring financial constraints:

Moving beyond the KZ index.’ Review of Financial Studies, 23(5): 1909-1940

Heller, Daniel, and Nicholas Vause (2012) ‘Collateral requirements for mandatory central clearing

of over-the-counter derivatives.’ BIS Working Papers, No 373

IMF (2010) ‘Making over-the-counter derivatives safer: The role of central counterparties.’ Interna-

tional Monetary Fund, Chapter 3, Global Financial Stability Report, April 2010

ISDA (1996) ‘Guidelines for collateral practitioners.’ International Swaps and Derivatives Associa-

tion

(1999) ‘Collateral review.’ Interntional Swaps and Derivatives Association

(2005) ‘Collateral guidelines.’ International Swaps and Derivatives Association

(2013) ‘Best practices for the otc derivatives collateral process.’ International Swaps and Deriva-

tives Association

Jankensgard, Hakan (2017) ‘Corporate hedging and collateral constraints: Evidence from a

difference-in-difference approach.’ Working Paper

Jin, Yanbo, and Philippe Jorion (2006) ‘Firm value and hedging: Evidence from US oil and gas

producers.’ Journal of Finance 61(2): 893-919

Johnson, Christian A. (2000) ‘Over-the-counter derivatives documentation: A practical guide for

executives.’ New York: Browne & Company

Keynes, J.M. (1936) ‘General theory of employment, interest and money.’ Palgrave Macmillan

Liberti, Jose M., and Atif R. Mian (2010) ‘Collateral spread and financial development.’ Journal of

Finance, 65(1): 147-177

Lins, Karl V., Henri Servaes, and Peter Tufano (2010) ‘What drives corporate liquidity? an in-

ternational survey of cash holdings and lines of credit.’ Journal of Financial Economics, 98(1):

160-176

27

LoPucki, Lynn M. (2003) ‘The myth of the residual owner: An empirical study.’ UCLA School of

Law, Law & Econ Research Paper No. 3-11

LoPucki, Lynn M., Arvin I. Abraham, and Bernd P. Delahaye (2012) ‘Optimizing English and

American security interest.’ Working Paper

Mello, Antonio S., and John E. Parsons (2013) ‘Margins, liquidity and the cost of hedging.’ Journal

of Applied Corporate Finance, 25(1): 34-43

Mian, Shehzad L (1996) ‘Evidence on corporate hedging policy.’ Journal of Financial and Quanti-

tative Analysis, 31(3): 419-439

Michaely, Roni, and Michael R. Roberts (2012) ‘Corporate dividend policies: Lessons from private

firms.’ The Review of Financial Studies, 25(3): 711-746

Miller, M.H., and D. Orr (1966) ‘A model of the demand for money by firms.’ Quarterly Journal of

Economics, 413-435

Nance, Deana R., Clifford W. Smith, and Charles W. Smithson (1993) ‘On the determinants of

corporate hedging.’ Journal of Finance, 48(1): 267-284

Opler, Tim, Lee Pinkowitz, Rene M. Stulz, and Rohan Williamson (1999) ‘The determinants and

implications of corporate cash holdings.’ Journal of Financial Economics, 52(1): 3-46.

Perez-Gonzalez, Francisco, and Hayong Yun (2013) ‘Risk management and firm value: Evidence

from weather derivatives.’ Journal of Finance, 68(5): 2143-2176

Rampini, Adriano A., Amir Sufi, and S. Viswanathan (2014) ‘Dynamic risk management.’ Journal

of Financial Economics, 111(2): 271-296

Rampini, Adriano A., and S. Viswanathan (2010) ‘Collateral, risk management, and the distribution

of debt capacity.’ Journal of Finance, 65(6): 2293-2322

Rauh, Joshua D., and Amir Sufi (2010) ‘Capital structure and debt structure.’ Review of Financial

Studies, 23(12): 4242-4280

Rosenbaum, Paul R., and Donald B. Rubin (1985) ‘Constructing a control group using multivariate

matched sampling methods thatincorporate the propensity score.’ The American Statistician,

39(1): 33-38

Servaes, Henri, Ane Tamayo, and Peter Tufano (2009) ‘The theory and practice of corporate risk

management.’ Journal of Applied Corporate Finance

28

Silva-Araujo, Gustavo, and Sergio Leao (2016) ‘OTC derivatives: Impacts of regulatory changes in

the non-financial sector.’ Journal of Financial Stability, 25: 132-149

Smith, Clifford W., and Rene M. Stulz (1985) ‘The determinants of firms hedging policies.’ Journal

of Financial and Quantitative Analysis, 20(4): 391-405

Smith, Jeffrey A., and Petra E. Todd (2005) ‘Does matching overcome lalondes critique of nonex-

perimental estimators?’ Journal of Econometrics, 125: 305-353

Tufano, Peter (1998) ‘The determinants of stock price exposure: Financial engineering and the gold

mining industry.’ Journal of Finance, 53(3): 1015-1052

29

Figure 1: States affected by hurricane Katrina’s track in 2005. This figure shows hurricaneKatrina’s track when it re-entered the U.S. on August 29, 2005. The most affected states wereFlorida, Louisiana and Mississippi. The data and the image are from the official Atlantic hurri-cane database (HURDAT), as provided by the National Oceanic and Atmospheric Administration’sHurricane Research Division.

Figure 2: Average Cash Holdings before and after hurricane Katrina for firms affectedversus not affected. This figure shows the pre- and post-treatment average cash holdings stan-dardized by total assets for firms in the treatment group versus firms in the control group. Thepost-treatment period considers fiscal year 2005, while the pre-treatment period 2004. The treat-ment group is defined as firm with their headquarters and production facilities located in the statesof Florida, Louisiana and Mississippi, while the rest of firms are part of the control group. AppendixA1 shows how Compustat variables have been constructed. Appendix A2 describes those variablescreated through manual gathering of data or through the text-search algorithm.

30

Figure 3: Counterparty Risk and Mitigation Strategies in Derivative Transactions forNon-financial Corporations.

Figure 4: Collateralization in Derivatives: Initial and Variation Margin. At the inceptionof the derivative transaction an initial margin can be pledged or received by the counterparties.Because of the mark-to-market nature of derivative transactions, during the life of the contract avariation margin can be required also for the party which is out-of-the-money.

Figure 5: Collateral Absorption in Financial Debt and derivative transactions. This figureshows collateral absorption patterns for debt and derivative transactions for U.S. public firms from1996 to 2015. In the case of financial debt, PPE the main source of collateral pledged (in orange),but up to all assets can be pledged if required (in grey). In the case of derivative transactions,cash and marketable securities are the main source of collateral (in orange). The size of the boxesdoes not correspond with the real absorption of collateral out of total assets that financial debt andderivatives exhaust, it is just an illustration.

31

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Table 1: Summary Statistics, Whole Sample. This tables reports summary statistics for allCompustat firms excluding financials (SIC codes 6000-6999) from 1996 to 2015. I) Firm Charac-teristics gather summary statistics from Compustat for the main firm-level variables used in theanalysis. II) Collateral Derivatives and III) Collateral Financial Debt are variables that have beengathered with a text-search algorithm looking for specific keywords in the SEC’s 10-K annual re-ports. Finally, IV) Financial Constraints reports the six financial constraints definitions in Almeidaet al. (2004) and Hadlock and Pierce (2010). Appendix A1 shows how Compustat variables havebeen constructed. Appendix A2 describes those variables created through manual gathering of dataor through the text-search algorithm.

I) Firm CharacteristicsMean Std Dev Median

Cashflows -0.010 0.253 0.060Profitability 0.036 0.258 0.101Market-to-Book 1.916 1.998 1.232Capital Expenditures 0.056 0.064 0.035R&D-to-Sales 0.692 2.907 0.001Acquisitions 0.030 0.074 0.000Cash Holdings 0.152 0.182 0.084Cash&MarketableSec 0.215 0.244 0.114Net Working Capital 0.063 0.197 0.044Log (Size) 5.819 2.014 5.690Log (Total Debt) 3.530 2.820 3.423Tangibility 0.260 0.238 0.176Book Leverage 0.221 0.226 0.173Market Leverage 0.220 0.247 0.132% Unsecured (Total Debt) 0.722 0.372 0.986

II) Collateral DerivativesMean Std Dev Median

Dummy Hedge 0.298 0.457 0.000Coll. Derivatives Dummy 0.012 0.110 0.000Coll. Derivatives Dummy, Pledged 0.010 0.098 0.000Coll. Derivatives Dummy, Received 0.004 0.063 0.000Coll. Pledged over Total Assets 0.001 0.131 0.000Cash Collateral %, Derivatives 0.769 0.389 1.000PPE Collateral %, Derivatives 0.003 0.036 0.000

III) Collateral Financial DebtMean Std Dev Median

Coll. Debt Dummy 0.558 0.497 1.000Coll. Pledged over Total Assets 0.460 0.159 1.000Cash Collateral %, Debt 0.164 0.345 0.000PPE Collateral %, Debt 0.572 0.452 0.710

IV) Financial ConstraintsMean Std Dev Median

FC#1: Dividend Payers 0.513 0.500 1.000FC#2: Size 0.250 0.433 0.000FC#3: S&P’s LTD Rating 0.740 0.439 1.000FC#4: Kaplan&Zingales Index 0.252 0.434 0.000FC#5: Size-Age Index 0.201 0.401 0.000FC#6: S&P’s STD Rating 0.929 0.256 1.000

# Observations 62,378

35

Tab

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.0029

2)(0

.00732)

(0.0

0105)

(0.0

0443)

Col

lDeb

t-0

.0084

0***

0.1

70*

**0.

000

615

0.02

33**

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ollD

ebt

-0.0

133

***

0.1

46***

0.0

00537

0.0

165***

(0.0

027

9)(0

.0110

)(0

.000

906)

(0.0

0364

)(0

.0027

0)(0

.00947)

(0.0

00800)

(0.0

0288)

FC

*C

ollD

ebt

-0.0

104*

**-0

.047

7***

-0.0

0085

20.

0078

3*F

C*C

ollD

ebt

-0.0

019

1-0

.00642

-0.0

0154

0.0

230***

(0.0

031

9)(0

.0107

)(0

.001

01)

(0.0

0420

)(0

.003

84)

(0.0

125)

(0.0

0126)

(0.0

0578)

FC

#2:

Siz

eF

C#

5:S

ize-

Age

Ind

exIn

dex

FC

0.0

043

10.

058

7***

-0.0

0064

0-0

.001

43F

C-0

.0098

5***

0.0

159**

-0.0

0147

0.0

00309

(0.0

054

0)(0

.0110

)(0

.001

47)

(0.0

0572

)(0

.003

71)

(0.0

0737)

(0.0

0105)

(0.0

0395)

Col

lDeb

t-0

.0065

1**

0.1

76*

**0.

000

502

0.02

25**

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ollD

ebt

-0.0

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***

0.1

66***

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00571

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.0026

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(0.0

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(0.0

0346)

FC

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ollD

ebt

-0.0

277*

**-0

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***

-0.0

0131

0.02

08**

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C*C

ollD

ebt

-0.0

157*

**-0

.0974***

-0.0

0182

0.0

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(0.0

053

9)(0

.0152

)(0

.001

50)

(0.0

0699

)(0

.004

90)

(0.0

143)

(0.0

0141)

(0.0

0631)

FC

#3:

LT

DR

atin

gF

C#

6:C

PR

ati

ng

FC

-0.0

008

320.0

449

***

-0.0

0067

4-0

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1***

FC

0.014

3***

0.0

511*

0.0

00733

-0.0

102

(0.0

0431

)(0

.014

0)(0

.001

66)

(0.0

0668

)(0

.0050

5)(0

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(0.0

0172)

(0.0

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Coll

Deb

t0.0

043

70.2

42*

**0.

0016

30.

0233

***

Col

lDeb

t0.0

050

80.2

58***

0.0

0432***

0.0

124

(0.0

0323

)(0

.016

2)(0

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31)

(0.0

0565

)(0

.0058

1)(0

.0326)

(0.0

0167)

(0.0

0876)

FC

*Coll

Deb

t-0

.0244

***

-0.1

31*

**-0

.001

990.

0037

3F

C*C

ollD

ebt

-0.0

201

***

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21***

-0.0

0441**

0.0

166*

(0.0

0391

)(0

.017

1)(0

.001

52)

(0.0

0632

)(0

.0060

0)(0

.0327)

(0.0

0176)

(0.0

0909)

36

Tab

le3:

Su

mm

ary

Sta

tist

ics,

by

Collate

ral

Requ

irem

ents

.T

his

tab

lere

por

tssu

mm

ary

stati

stic

sfo

rth

ere

leva

nt

firm

chara

cter

isti

csso

rtin

gby

i)co

llat

eral

requ

irem

ents

for

deb

t(“

Yes

”/“N

o”),

ii)

coll

ater

alre

qu

irem

ents

for

der

ivat

ives

(“Y

es”/“N

o”),

iii)

coll

ate

ral

cred

ittr

igger

(“Y

es”/“

No”)

an

div

)co

llate

ral

requ

irem

ents

for

both

fin

anci

ald

ebt

and

der

ivat

ive

tran

sact

ion

s(“

Yes

”/“N

o”)

for

all

Com

pu

stat

firm

sex

clu

din

gfi

nan

cials

(SIC

cod

es600

0-699

9)fr

om19

96to

201

5.A

pp

end

ixA

1sh

ows

how

Com

pu

stat

vari

able

sh

ave

bee

nco

nst

ruct

ed.

Ap

pen

dix

A2

des

crib

esth

ose

vari

able

scr

eate

dth

rou

gh

man

ual

gath

erin

gof

dat

aor

thro

ugh

the

text-

sear

chal

gori

thm

.

Coll

Deb

t?C

oll

Der

ivat

ives

?C

oll

Tri

gge

rD

eriv

ati

ves?

Coll

Both

?Y

esN

oY

esN

oY

esN

oY

esN

oM

ean

sdM

ean

sdM

ean

sdM

ean

sdM

ean

sdM

ean

sdM

ean

sdM

ean

sd

Cash

flow

s-0

.017

0.249

-0.0

08

0.2

540.

040

0.11

8-0

.011

0.25

40.

051

0.0

42

-0.0

11

0.2

53

0.0

44

0.1

45

-0.0

08

0.2

56

Pro

fita

bil

ity

0.026

0.2

51

0.0

40

0.26

00.

091

0.12

70.

035

0.25

90.

100

0.045

0.0

36

0.2

58

0.0

98

0.1

62

0.0

40

0.2

62

Mark

et-t

o-b

ook

1.7

17

1.753

2.000

2.0

862.

151

3.08

91.

913

1.98

02.

048

3.1

68

1.9

16

1.9

91

1.3

15

1.4

17

1.9

92

2.0

61

Cap

ital

Exp

end

itu

res

0.0

50

0.058

0.059

0.0

670.

067

0.06

10.

056

0.06

40.

071

0.0

44

0.0

56

0.0

64

0.0

62

0.0

57

0.0

58

0.0

67

R&

Dto

Sale

s0.6

15

2.6

57

0.7

25

3.00

60.

134

1.24

80.

699

2.92

10.

004

0.019

0.6

95

2.9

13

0.0

66

0.3

95

0.7

30

3.0

16

Acq

uis

itio

ns

0.0

28

0.071

0.031

0.0

750.

022

0.06

20.

030

0.07

40.

012

0.0

36

0.0

30

0.0

74

0.0

27

0.0

67

0.0

31

0.0

75

Cash

Hol

din

gs

0.1

46

0.171

0.155

0.1

870.

074

0.11

20.

153

0.18

30.

060

0.1

65

0.1

52

0.1

82

0.0

80

0.0

98

0.1

55

0.1

87

Cash

&M

arke

tab

leS

ec0.2

01

0.231

0.221

0.2

490.

096

0.13

50.

217

0.24

50.

042

0.0

76

0.2

16

0.2

44

0.1

03

0.1

28

0.2

22

0.2

49

Net

Wor

kin

gC

apit

al0.0

89

0.197

0.052

0.19

60.

013

0.16

60.

064

0.19

7-0

.030

0.064

0.0

64

0.1

97

0.0

30

0.1

74

0.0

53

0.1

96

Log

(Siz

e)5.8

00

1.9

70

5.8

28

2.0

328.

769

1.72

95.

783

1.99

19.

264

1.1

69

5.8

05

2.0

05

8.6

82

1.9

38

5.7

97

2.0

14

Tan

gib

ilit

y0.2

43

0.214

0.267

0.24

80.

482

0.26

50.

257

0.23

70.

627

0.182

0.2

59

0.2

37

0.4

19

0.2

65

0.2

64

0.2

46

Book

Lev

erage

0.2

47

0.2

29

0.2

11

0.22

40.

343

0.18

80.

220

0.22

60.

346

0.108

0.2

21

0.2

26

0.3

47

0.2

09

0.2

09

0.2

24

Mark

etL

ever

age

0.2

45

0.248

0.210

0.24

60.

442

0.27

00.

217

0.24

60.

465

0.231

0.2

19

0.2

47

0.3

64

0.2

23

0.2

07

0.2

44

%U

nse

cure

d0.

629

0.3

94

0.7

61

0.35

50.

814

0.32

20.

721

0.37

30.

968

0.140

0.7

21

0.3

73

0.7

75

0.3

35

0.7

60

0.3

56

Coll

.D

ebt

Du

mm

y0.

679

0.4

67

0.5

07

0.5

000.

441

0.49

70.

559

0.49

60.

194

0.3

97

0.5

60

0.4

96

0.5

79

0.4

95

0.5

09

0.5

00

Du

mm

yH

edge

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0.4

99

0.1

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000

0.00

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289

0.45

31.

000

0.0

00

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95

0.4

56

1.0

00

0.0

00

0.1

90

0.3

92

Coll

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ivat

ives

Du

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0.10

01.

000

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00.

000

0.00

00.

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0.498

0.0

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0.1

01

1.0

00

0.0

00

0.0

00

0.0

00

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dged

Du

mm

y0.

014

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0.0

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0.09

00.

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0.39

70.

000

0.00

00.

389

0.488

0.0

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0.0

90

0.8

07

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95

0.0

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eive

dD

um

my

0.005

0.0

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0.0

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325

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000

0.00

00.

147

0.355

0.0

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0.0

58

0.3

05

0.4

61

0.0

00

0.0

00

FC

#1:

Div

iden

ds

0.5

54

0.4

97

0.4

96

0.5

000.

288

0.45

30.

516

0.50

00.

119

0.3

24

0.5

15

0.5

00

0.2

70

0.4

45

0.4

98

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00

FC

#2:

Siz

e0.

254

0.4

35

0.2

48

0.4

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50.

253

0.43

50.

000

0.0

00

0.2

51

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0.0

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0.1

38

0.2

51

0.4

33

FC

#3:

LT

DR

atin

g0.7

31

0.444

0.744

0.4

360.

240

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746

0.43

50.

063

0.2

44

0.7

43

0.4

37

0.2

93

0.4

56

0.7

50

0.4

33

FC

#4:

K&

ZIn

dex

0.2

95

0.456

0.233

0.42

30.

175

0.38

10.

252

0.43

40.

079

0.271

0.2

52

0.4

34

0.1

77

0.3

82

0.2

34

0.4

23

FC

#5:

Siz

e-A

ge0.2

16

0.4

12

0.1

95

0.3

960.

018

0.13

50.

204

0.40

30.

000

0.0

00

0.2

02

0.4

02

0.0

23

0.1

49

0.1

97

0.3

98

FC

#6:

ST

DR

atin

g0.9

58

0.201

0.918

0.27

50.

669

0.47

10.

933

0.25

10.

409

0.493

0.9

32

0.2

53

0.7

46

0.4

36

0.9

21

0.2

70

18,

460

43,

918

758

61,6

2025

2455

311

43,4

71

37

Table 4: Corporate policy decisions by debt and derivatives collateral requirements. Thistable reports summary statistics (mean and standard deviation in parenthesis) for cash holdings,acquisitions, dividends, investment and R&D investment sorted by the debt and derivatives collateralrequirements (“Yes”/“No”/“All Obs”) for all Compustat firms excluding financials (SIC codes 6000-6999) from 1996 to 2015. Appendix A1 shows how Compustat variables have been constructed.Appendix A2 describes those variables created through manual gathering of data or through thetext-search algorithm.

Cash Holdings Cash&MarketableSecColl. Derivatives Dummy Coll. Derivatives Dummy

Coll. Debt Dummy No Yes Total Coll. Debt Dummy No Yes Total

No 0.155 0.070 0.155 No 0.222 0.091 0.221(0.187) (0.120) (0.187) (0.249) (0.139) (0.249)43,471 447 43,918 43,471 447 43,918

Yes 0.147 0.080 0.146 Yes 0.203 0.103 0.201(0.172) (0.098) (0.171) (0.232) (0.128) (0.231)18,149 311 18,460 18,149 311 18,460

Total 0.153 0.074 0.152 Total 0.217 0.096 0.215(0.183) (0.112) (0.182) (0.245) (0.135) (0.244)61,620 758 62,378 61,620 758 62,378

Acquisitions Dividends over Total AssetsColl. Derivatives Dummy Coll. Derivatives Dummy

Coll. Debt Dummy No Yes Total Coll. Debt Dummy No Yes Total

No 0.031 0.019 0.031 No 0.009 0.015 0.010(0.075) (0.058) (0.075) (0.023) (0.024) (0.023)43,471 447 43,918 43,471 447 43,918

Yes 0.028 0.027 0.028 Yes 0.008 0.017 0.008(0.071) (0.067) (0.071) (0.023) (0.028) (0.023)18,149 311 18,460 18,149 311 18,460

Total 0.030 0.022 0.030 Total 0.009 0.016 0.009(0.074) (0.062) (0.074) (0.023) (0.026) (0.023)61,620 758 62,378 61,620 758 62,378

Capital Expenditures R&D InvestmentColl. Derivatives Dummy Coll. Derivatives Dummy

Coll. Debt Dummy No Yes Total Coll. Debt Dummy No Yes Total

No 0.058 0.070 0.059 No 0.730 0.182 0.725(0.067) (0.063) (0.067) (3.016) (1.591) (3.006)43,471 447 43,918 43,471 447 43,918

Yes 0.049 0.062 0.050 Yes 0.624 0.066 0.615(0.058) (0.057) (0.058) (2.678) (0.395) (2.657)18,149 311 18,460 18,149 311 18,460

Total 0.056 0.067 0.056 Total 0.699 0.134 0.692(0.064) (0.061) (0.064) (2.921) (1.248) (2.907)61,620 758 62,378 61,620 758 62,378

38

Tab

le5:

Identi

ficati

on

,S

hock

toL

iqu

idit

y.

Resp

on

seof

the

Exte

nsi

ve

Marg

inof

Hed

gin

g,

Invest

ment

an

dC

ollate

ral

for

Deri

vati

ves.

Th

ista

ble

rep

orts

DID

esti

mati

onre

sult

sfo

rth

eeff

ect

ofhu

rric

ane

Kat

rin

aon

the

exte

nsi

vem

arg

inof

hed

gin

g,

inves

tmen

tan

dco

llat

eral

for

der

ivat

ives

for

firm

sin

the

trea

tmen

tgro

up

asco

mp

ared

toth

eco

ntr

olgr

oup

.W

ed

efin

etw

oalt

ern

ati

vetr

eatm

ent

gro

up

s.Treat1

con

sid

ers

firm

loca

ted

inK

atri

na

state

s(L

A,

MS

an

dF

L),

wh

ileTreat2

con

sid

ers

firm

slo

cate

din

LA

an

dM

San

dfi

rms

loca

ted

inci

ties

inF

Laff

ecte

dby

Katr

ina.

We

excl

ud

efi

rms

wit

han

ass

etim

pai

rmen

tfr

omth

etr

eatm

ent

grou

p.

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ere

stof

firm

-yea

rob

serv

ati

ons

bel

on

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the

contr

ol

gro

up

.T

he

pre

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atm

ent

per

iod

con

sid

ers

fisc

alyea

r20

03-0

4,w

hil

eth

ep

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tmen

tp

erio

dco

nsi

der

s2005.

***,

**,

an

d*

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ote

stati

stic

al

sign

ifica

nce

at

the

1%

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d10

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vels

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spec

tive

ly.

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pen

dix

A1

show

sh

owC

omp

ust

atva

riab

les

hav

eb

een

con

stru

cted

.A

pp

endix

A2

des

crib

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ose

vari

ab

les

crea

ted

thro

ugh

man

ual

gath

erin

gof

dat

aor

thro

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text-

sear

chal

gor

ith

m.

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end

ent

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iab

les:

Du

mm

yH

edge

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ital

Exp

end

itu

res

Coll

ate

ral

Der

ivati

ves

Du

mm

y(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

(11)

(12)

Trea

t1∗Post

t-1

.844

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97*

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0.06

12-0

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733**

(0.3

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(0.3

07)

(0.2

41)

(0.2

06)

(0.0

340)

(0.0

345)

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t2∗Post

t-2

.048*

**-1

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***

0.25

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418

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35**

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37**

(0.3

17)

(0.3

87)

(0.2

86)

(0.2

28)

(0.0

567)

(0.0

568)

Pro

fita

bilit

y-0

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***

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51*

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29**

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03**

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29**

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03**

-0.0

00434

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00422

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00539

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7)(0

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(0.0

0205)

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0177)

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0198)

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Mark

et-t

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ook

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566

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559

***

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566*

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9***

0.03

56**

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0350

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56**

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350

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-0.0

00334

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00330

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(0.0

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116

)(0

.011

7)(0

.011

6)(0

.011

9)(0

.011

8)(0

.012

0)(0

.0118

)(0

.000302)

(0.0

00303)

(0.0

00301)

(0.0

00303)

Book

Lev

erage

0.1

58*

**0.1

78*

**0.

160

***

0.17

9***

-0.0

365

-0.0

462

-0.0

366

-0.0

464

0.0

0207

0.0

0186

0.0

0216

0.0

0195

(0.0

593)

(0.0

611

)(0

.058

7)(0

.060

7)(0

.214

)(0

.216

)(0

.214

)(0

.216

)(0

.00174)

(0.0

0130)

(0.0

0178)

(0.0

0131)

Siz

e-0

.247

-0.1

41

-0.2

44

-0.1

400.

478*

0.37

20.

477*

0.371

-0.0

0290

-0.0

0318

-0.0

0270

-0.0

0301

(0.1

76)

(0.1

68)

(0.1

76)

(0.1

68)

(0.2

68)

(0.2

66)

(0.2

68)

(0.2

66)

(0.0

0733)

(0.0

0709)

(0.0

0718)

(0.0

0699)

Rate

d4.

394

**4.

427

**4.3

84*

*4.4

21**

0.32

00.

365

0.32

30.

368

0.0

00776

-0.0

0340

-7.5

0e-

05

-0.0

0424

(1.7

58)

(1.7

65)

(1.7

60)

(1.7

66)

(0.4

66)

(0.4

58)

(0.4

66)

(0.4

59)

(0.0

0749)

(0.0

141)

(0.0

0752)

(0.0

143)

CollD

ebt

EM

0.9

41*

0.983

*0.

934

*0.

978*

0.30

2*0.

342*

0.30

2*0.3

43*

0.0

137

0.0

170

0.0

135

0.0

167

(0.5

15)

(0.5

14)

(0.5

15)

(0.5

15)

(0.1

73)

(0.1

80)

(0.1

73)

(0.1

80)

(0.0

134)

(0.0

147)

(0.0

133)

(0.0

146)

Clu

ster

edS

ES

tate

Sta

teS

tate

Sta

teS

tate

Sta

teS

tate

Sta

teS

tate

Sta

teS

tate

Sta

teF

irm

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rF

EY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oY

esN

oIn

du

stry

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FE

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Yes

No

Yes

No

Yes

No

Yes

No

Yes

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30,5

92

30,5

92

30,5

92

39

Tab

le6:

Identi

ficati

on

,S

hock

toL

iqu

idit

y.

Resp

on

seof

the

Exte

nsi

ve

Marg

inof

Deri

vati

ve

Inst

rum

ents

by

Exp

osu

rean

dT

yp

e.

Th

ista

ble

rep

orts

DID

esti

mat

ion

resu

lts

for

the

effec

tof

hu

rric

ane

Kat

rin

aon

the

exte

nsi

ve

mar

gin

of

der

ivati

vein

stru

men

ts,

incl

ud

ing;

fore

ign

exch

an

ge,

inte

rest

rate

,co

mm

od

ity,

op

tion

s,fu

ture

s,fo

rwar

ds,

swap

san

dot

her

typ

eof

inst

rum

ents

.T

he

trea

tmen

tgro

up

con

sid

ers

firm

slo

cate

din

LA

and

MS

and

firm

slo

cate

din

citi

esin

FL

affec

ted

by

Kat

rin

a(Treat2

).W

eex

clu

de

firm

sw

ith

an

ass

etim

pair

men

tfr

om

the

trea

tmen

tgro

up

.T

he

rest

of

firm

-yea

rob

serv

ati

ons

bel

on

gto

the

contr

olgr

oup

.T

he

pre

-tre

atm

ent

per

iod

con

sid

ers

fisc

al

year

2003-0

4,

wh

ile

the

post

-tr

eatm

ent

per

iod

con

sid

ers

200

5.***

,**,

and

*d

enot

est

atis

tica

lsi

gnifi

can

ceat

the

1%,

5%,

and

10%

leve

ls,

resp

ecti

vely

.A

pp

end

ixA

1sh

ows

how

Com

pu

stat

vari

ab

les

hav

eb

een

con

stru

cted

.A

pp

end

ixA

2d

escr

ibes

thos

eva

riab

les

crea

ted

thro

ugh

manu

al

gath

erin

gof

data

or

thro

ugh

the

text-

sear

chal

gor

ith

m.

Dep

end

ent

Var

iab

les:

Exte

nsi

veM

argi

nof

Der

ivat

ive

Inst

rum

ents

FX

IRC

PO

pti

ons

Fu

ture

sF

orw

ard

sS

wap

sO

ther

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Treat∗Post t

-1.9

63**

*0.

189

-0.1

57-1

.582

***

-0.1

80**

0.0

289

-0.4

93

-0.5

79***

(0.2

93)

(0.2

09)

(0.4

96)

(0.4

13)

(0.0

869)

(0.2

96)

(0.7

58)

(0.1

28)

Post t

1.1

81

0.44

60.

343

0.54

70.

133

0.822

0.6

01

0.0

601

(0.7

90)

(0.4

95)

(0.3

24)

(0.8

31)

(0.2

77)

(0.5

58)

(0.4

46)

(0.3

01)

Pro

fita

bil

ity

-0.0

796*

*-0

.041

7*0.

0234

-0.0

511

-0.0

0385

-0.0

0274

-0.0

323*

-0.0

210

(0.0

340

)(0

.022

4)(0

.048

9)(0

.035

8)(0

.005

12)

(0.0

528

)(0

.0176)

(0.0

145)

Mark

et-t

o-b

ook

-0.0

136

*-0

.007

65-0

.005

96-0

.012

1-0

.000

569

-0.0

0542

-0.0

0649**

-0.0

0393

(0.0

0814

)(0

.004

64)

(0.0

0425

)(0

.008

16)

(0.0

0267

)(0

.004

00)

(0.0

0282)

(0.0

0242)

Book

Lev

erage

0.0

351

-0.0

208

0.04

320.

0463

-0.0

119*

0.01

45-0

.00633

-0.0

307

(0.0

616

)(0

.041

3)(0

.056

2)(0

.050

2)(0

.007

06)

(0.0

648

)(0

.0287)

(0.0

241)

Siz

e0.0

460

-0.0

489

-0.0

369

-0.0

312

-0.0

157

-0.0

0444

-0.0

0439

-0.0

195

(0.1

24)

(0.0

892)

(0.0

803)

(0.1

11)

(0.0

507)

(0.0

880

)(0

.0740)

(0.0

388)

Rat

ed2.

969

*0.

786

-0.0

206

2.87

9*0.

412*

1.6

78

-0.8

31

0.0

604

(1.6

88)

(1.0

04)

(0.6

98)

(1.4

82)

(0.2

45)

(1.0

87)

(1.1

79)

(0.4

60)

Coll

Deb

tE

M0.

508

0.6

43**

*0.

321

0.80

8**

0.08

920.4

38*

0.3

85*

-0.0

277

(0.4

07)

(0.2

22)

(0.1

95)

(0.3

50)

(0.0

571)

(0.2

39)

(0.1

97)

(0.1

06)

Clu

ster

edS

ES

tate

Sta

teS

tate

Sta

teS

tate

Sta

teS

tate

Sta

teF

irm

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rF

EY

esY

esY

esY

esY

esY

esY

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du

stry

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92

30,

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40

Table 7: Robustness Check. Identification, Shock to Real Estate. Response of theExtensive Margin of Hedging, Collateral for Derivatives and Investment. This table re-ports the empirical link between the value of real estate assets and the extensive margin of hedging,collateral for derivatives and investment. We build on a similar identification setup as in Chaneyet al. (2012). Columns (1) and (2) use the hedging dummy, Columns (3) and (4) the collateralfor derivatives dummy and Columns (5) and (6) investment as a dependent variable. We reportthe results for the state-level residential price index and the MSA-level residential prices for eachdependent variable. We control for firm-level initial characteristics (5 quintiles of Age, Asset, Lever-age and ROA as well as initial 2-digit industry and state of location) interacted with Real EstatePrices. We also control for observable firm characteristics as Cash Holdings, Market-to-book, Age,Profitability, Dividend Payout, Net Debt and Leverage. All specifications use year and firm fixedeffects and cluster observations at the state-year level. ***, **, and * denote statistical significanceat the 1%, 5%, and 10% levels, respectively. Appendix A1 shows how Compustat variables havebeen constructed. Appendix A2 describes those variables created through manual gathering of dataor through the text-search algorithm.

Dependent Variables:Dummy Hedge Collateral Derivatives EM Capital Expenditures

State MSA State MSA State MSA(1) (2) (3) (4) (5) (6)

RE Value State 0.205 -0.00516 0.0347***(0.253) (0.00799) (0.00496)

Mean Index State -27.83*** 0.0741 -0.105(8.964) (0.894) (0.105)

RE Value, MSA 0.182 -0.00580 0.0334***(0.227) (0.00712) (0.00481)

Mean Index MSA -24.55*** 0.321 -0.0879(7.166) (0.812) (0.0853)

Cash Holdings 0.124*** 0.131*** -0.00336 -0.00145 -0.0175*** -0.0170***(0.0358) (0.0342) (0.00267) (0.00104) (0.00247) (0.00246)

Market-to-book -0.0442 -0.0305 0.00325 0.0123 0.00873*** 0.00834***(0.124) (0.114) (0.0166) (0.0165) (0.00212) (0.00211)

Log (Size) 3.355*** 3.563*** 0.0405 0.0181 0.0891*** 0.0928***(0.655) (0.599) (0.0506) (0.0428) (0.0143) (0.0137)

Age 3.673*** 3.430*** -0.00963 -0.0250 -0.0204*** -0.0213***(0.470) (0.380) (0.0439) (0.0359) (0.00540) (0.00452)

Profitability -4.618*** -4.846*** -0.0461 -0.0159 0.247*** 0.240***(0.994) (0.920) (0.0606) (0.0433) (0.0386) (0.0392)

Dividend Payout 0.0259 0.0622** -0.000139 -7.85e-05 8.58e-06 7.37e-05(0.0225) (0.0258) (0.000134) (0.000245) (0.000120) (0.000185)

Net Debt -0.0686 -0.139 -0.0114 -0.00618** 0.0114*** 0.0112***(0.106) (0.104) (0.00709) (0.00312) (0.00223) (0.00219)

Leverage 1.257 1.684 0.0146 -0.00877 -0.0314 -0.0220(1.344) (1.216) (0.0629) (0.0595) (0.0298) (0.0303)

Clustered SE State-Firm State-Firm State-Firm State-Firm State-Firm State-FirmFirm FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 18,550 18,550 18,550 18,550 18,550 18,550

41

Tab

le8:

Rob

ust

ness

Ch

eck

s.S

hock

toL

iqu

idit

yw

ith

Pro

pen

sity

Score

Matc

hin

g.

Th

ista

ble

rep

ort

sD

IDes

tim

ati

on

resu

lts

when

we

ap

ply

Pro

pen

sity

Sco

reM

atc

hin

gas

in?.

We

look

at

the

effec

tof

hu

rric

ane

Kat

rin

aon

the

exte

nsi

vem

arg

inof

hed

gin

g,

coll

ate

ral

for

der

ivati

ves

,co

llat

eralcr

edit

trig

ger

s,fu

ture

s,op

tion

s,ca

shh

old

ings

,in

vest

men

tan

dth

ein

ten

sive

mar

gin

of

coll

ater

alfo

rd

eriv

ati

ves.

As

inth

eb

ase

lin

ere

sult

s,w

ed

efin

etw

oal

tern

ati

ve

trea

tmen

tgr

ou

ps.Treat1

con

sid

ers

firm

loca

ted

inK

atri

na

stat

es(L

A,M

San

dF

L),

wh

ileTreat2

con

sid

ers

firm

slo

cate

din

LA

an

dM

San

dfi

rms

loca

ted

inci

ties

inF

Laff

ecte

dby

Kat

rin

a.W

eex

clu

de

firm

sw

ith

anas

set

imp

air

men

tfr

om

the

trea

tmen

tgro

up

.T

he

rest

of

firm

-yea

rob

serv

ati

ons

bel

on

gto

the

contr

olgr

oup.

Th

ep

re-t

reat

men

tp

erio

dco

nsi

der

sfi

scal

year

2003-0

4,

wh

ile

the

post

-tre

atm

ent

per

iod

con

sid

ers

2005

.**

*,**,

and

*d

enot

est

ati

stic

al

sign

ifica

nce

atth

e1%

,5%

,an

d10

%le

vels

,re

spec

tive

ly.

Ap

pen

dix

A1

show

sh

owC

om

pu

stat

vari

able

sh

ave

bee

nco

nst

ruct

ed.

Ap

pen

dix

A2

des

crib

esth

ose

vari

able

scr

eate

dth

rou

ghm

anu

algat

her

ing

of

data

or

thro

ugh

the

text-

searc

hal

gor

ith

m.

Pan

el

A)

Tre

atm

ent

Gro

up

:fi

rms

loca

ted

inL

A,

MS

and

FL

stat

es.

Dep

end

ent

Var

iab

les

for

DID

wit

hP

SM

:D

um

my

Hed

geC

oll

Der

EM

Tri

gger

EM

Fu

ture

Op

tion

Cash

Cap

exC

oll

Der

IM(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Tre

at1*P

ost

-6.9

72**

*-0

.052

3***

-0.0

392*

**-0

.268

***

-0.9

84**

*-3

.739***

0.2

25

-0.0

0314

(2.0

91)

(0.0

131)

(0.0

113)

(0.0

297)

(0.2

97)

(1.4

32)

(0.6

52)

(0.0

0260)

#O

bse

rvat

ion

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592

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el

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Tre

atm

ent

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up

:fi

rms

loca

ted

inL

Aan

dM

San

dfi

rms

inci

ties

aff

ecte

dby

Katr

ina

inF

Lst

ate

.

Dep

end

ent

Var

iab

les

for

DID

wit

hP

SM

:D

um

my

Hed

ge

Col

lDer

EM

Tri

gger

EM

Fu

ture

Op

tion

Cash

Cap

exC

oll

Der

IM(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Tre

at2*P

ost

-8.0

64**

*-0

.052

3***

-0.0

425*

**-0

.271

***

-1.1

90**

-3.7

39***

0.1

98

-0.0

0339

(3.0

00)

(0.0

131)

(0.0

122)

(0.0

301)

(0.5

04)

(1.4

32)

(0.8

34)

(0.0

0259)

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42

Table 9: Robustness Check. Placebo Katrina Identification in Fiscal Year 2002. Re-sponse of the Extensive Margin of Hedging, Cash Holdings and Collateral for Deriva-tives. This table reports DID estimation results for the placebo effect of hurricane Katrina in fiscalyear 2002. We report the effect on cash holdings, the extensive margin of hedging and collateral forderivatives and investment for firms in the treatment group as compared to the control group. Thetreatment groups for all the regression specifications consider firms located in Katrina states. Weexclude firms with an asset impairment from the treatment group. The rest of firm-year observa-tions belong to the control group. The pre-treatment period considers fiscal year 2000-01, while thepost-treatment period considers 2002. ***, **, and * denote statistical significance at the 1%, 5%,and 10% levels, respectively. Appendix A1 shows how Compustat variables have been constructed.Appendix A2 describes those variables created through manual gathering of data or through thetext-search algorithm.

Dependent Variables:Cash Hedge CollDer Capex(1) (2) (3) (4)

Treat ∗ Postt -0.665 0.770 0.222 0.335(1.233) (1.802) (0.300) (0.617)

Postt 1.801** -0.420 -0.00956 -2.458***(0.798) (0.627) (0.272) (0.390)

Profitability 0.431* -0.257** -0.0186* -0.711***(0.248) (0.117) (0.00971) (0.127)

Market-to-book 0.0148 -0.0244*** -0.00121 0.0357***(0.00907) (0.00943) (0.00142) (0.00484)

Book Leverage -2.030*** 0.0544 -0.0122 -0.299(0.452) (0.226) (0.0164) (0.197)

Size -3.244*** 0.504** 0.0388 0.240(0.356) (0.231) (0.0330) (0.152)

Rated 0.233 3.064** 0.0506 -0.191(0.609) (1.471) (0.0749) (0.318)

CollDebt -2.177*** 1.698*** -0.102* 0.236(0.357) (0.500) (0.0608) (0.160)

CollDer -0.00568 0.923*** -0.0104(0.0101) (0.0438) (0.0196)

Clustered SE State State State StateFirm FE Yes Yes Yes YesYear FE No No No NoIndustry*Year FE Yes Yes Yes Yes# Observations 41,935 41,935 41,935 41,935

43

Table 10: Robustness Checks. Collateral Requirements and the Level of Cash Holdings.This table reports OLS estimation results for the effect of collateral requirements in financial debtand derivative transaction on the level of cash holdings using the 62,378 observations from 1996 to2015. This Table resembles Table 6 in Opler et al. (1999). We use the extensive margin of collateralfor derivatives (Columns (1) and (2)), cash-collateral pledged over total assets (Columns (3) and(4)) and total collateral pledged over total assets (Columns (5) and (26)). Collateral requirementsfor debt is a dummy variable. We use the volatility of cashflows, a hedging dummy, size, cashflows,market-to-book, net working capital, book leverage, capital expenditures, acquisitions, dividends, acredit line dummy, share repurchases and RD-to-sales as in every specification as controls. Some ofthese controls are omitted for presentation purposes. We saturate the specification with firm andyear fixed effects. Standard errors are clustered at the firm level. ***, **, and * denote statisticalsignificance at the 1%, 5%, and 10% levels, respectively. Appendix A1 shows how Compustatvariables have been constructed. Appendix A2 describes those variables created through manualgathering of data or through the text-search algorithm.

Dependent Variable:Cash Holdings over Total Assets

(1) (2) (3) (4) (5) (6)

CollDer EM -0.0173*** -0.0177***(0.00581) (0.00579)

CollDerCash IM -0.247*** -0.239***(0.0817) (0.0821)

CollDer IM -0.339*** -0.328***(0.103) (0.105)

CollDebt EM -0.00182 -0.00176 -0.00169 -0.00162 -0.00172 -0.00165(0.00205) (0.00203) (0.00203) (0.00202) (0.00203) (0.00202)

σ(Ind) 2.68e-05 2.62e-05 2.62e-05(7.57e-05) (7.54e-05) (7.54e-05)

σ(CF ) 0.00202 0.00202 0.00202(0.00130) (0.00130) (0.00130)

Dummy Hedge 0.00138 0.000871 0.000689 0.000169 0.000718 0.000197(0.00212) (0.00211) (0.00210) (0.00209) (0.00210) (0.00209)

Size -0.0242*** -0.0238*** -0.0242*** -0.0238*** -0.0242*** -0.0238***(0.00192) (0.00194) (0.00192) (0.00194) (0.00192) (0.00194)

Market-to-book 0.00544*** 0.00551*** 0.00545*** 0.00552*** 0.00545*** 0.00551***(0.000670) (0.000707) (0.000670) (0.000707) (0.000670) (0.000707)

Book leverage -0.00270 -0.00369 -0.00266 -0.00366 -0.00265 -0.00365(0.00696) (0.00701) (0.00696) (0.00702) (0.00696) (0.00702)

Capital Expenditures -0.109*** -0.104*** -0.110*** -0.105*** -0.110*** -0.105***(0.0156) (0.0154) (0.0156) (0.0154) (0.0156) (0.0154)

CreditL EM -0.00166 -0.00187 -0.00146 -0.00166 -0.00145 -0.00165(0.00211) (0.00211) (0.00211) (0.00211) (0.00211) (0.00211)

R&D-to-Sales 0.000830 0.000850 0.000828 0.000850 0.000828 0.000851(0.000892) (0.000937) (0.000892) (0.000937) (0.000892) (0.000937)

Clustered SE Firm Firm Firm Firm Firm FirmFirm FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes YesObservations 62,378 62,378 62,378 62,378 62,378 62,378

44

Appendix

Appendix A1: Variable Definition

Compustat:

Cash Holdings: Cash over Total assets.

Cash&MarketableSec: Cash and short-term investments over Total assets.

TotDebt: Debt in Current Liabilities plus Long-Term Debt.

Book Leverage: Book leverage. Total Debt over Total Assets.

Tangibility: Tangibility. Property, Plant, and Equipment, Net over Total Assets.

Size: Total assets, Total Assets in Million USD.

Profitability: Profitability. Operating Income Before Depreciation over Total assets.

Market-to-book: Market Value of Equity plus Total Debt plus Preferred Stock Liquidating Value

minus Deferred Taxes and Investment Tax Credit over Total assets.

Rated: Dummy variable, takes a value of 1 if the firm-year observation has an S&P Long-Term

Bond Rating.

DivPayer: Dummy variable, takes a value of 1 if the firm-year observation has a positive value for

Common Dividends.

Capital Expenditures: Capital Expenditures over Total Assets.

Cashflows: Cashflows. Earnings before extraordinary items and depreciation (minus dividends)

over total assets.

R&D-to-Sales: R&D expenses over Sales.

Acquisitions: Acquisitions over total assets.

MVE: Market value of equity. Stock price times Common Shares Used to Calculate Earnings Per

Share.

Market Leverage: Market leverage. Total Debt over Total Debt plus MV Equity.

% Unsecured: Total Debt Minus Mortgages and Other Secured Debt (item 241) over Total Debt.

FC#1 Payout Ratio: Dummy variable that takes a value of 1 if the firm-year’s payout ratio

(dividends plus repurchases over operating income) is within the first quartile of the distribution.

FC#2 Size: Dummy variable that takes a value of 1 if the firm-year’s total assets is within the

first quartile of the distribution.

FC#3 Rated: Dummy variable that takes a value of 1 if the firm-year observation has a S&P’s

long term debt credit rating.

FC#4 K&Z Index: Dummy variable that takes a value of 1 if the firm-year’s Kaplan&Zingales

Index is within the fourth quartile of the distribution.

FC#5 S-A Index: Dummy variable that takes a value of 1 if the firm-year’s Size-Age Index is

within the fourth quartile of the distribution.

FC#6 ST Rated: Dummy variable that takes a value of 1 if the firm-year observation has a

S&P’s commercial paper credit rating.

Other Variables:

Dummy Hedge: Dummy variable for the extensive margin derivatives. It takes a value of 1 if the

firm-year uses derivatives for hedging purposes.

Treat1: Dummy variable for firms located in Katrina states in the pre-treatment period of the

Katrina identification strategy. It takes a value of 1 if the firm was located in Florida (FL),

Louisiana (LA) and Mississippi (MS) in the pre-treatment period.

Treat2: Dummy variable for firms located in Katrina states in the pre-treatment period of the

Katrina identification strategy. It takes a value of 1 if the firm was located in Louisiana (LA) or

Mississippi (MS) in the pre-treatment period. For Florida (FL) state, it takes the value of 1 if the

firm was located in a city affected by the hurricane.

Post: Dummy variable that takes the value of 1 for the period after hurricane Katrina in fiscal

year 2005.

Collateral Derivatives or CollDer EM: Dummy variable for the extensive margin of collateral

for derivatives. It takes a value of 1 if the firm-year observation pledging collateral for derivatives.

CollDer IM: Intensive margin of collateral for derivatives. Total collateral pledged for derivative

transactions over total assets.

Collateral Debt or CollDebt EM: Dummy variable for the extensive margin of collateral for

debt. It takes a value of 1 if the firm-year observation pledging collateral for debt.

CollDebt IM: Intensive margin of collateral for debt. Total collateral pledged for debt over total

assets.

Trigger: Dummy variable for whether there is collateral trigger for derivatives. It takes a value of

1 if the firm-year observation has a clause which requires the pledge of collateral for derivatives if

and only if the credit rating goes beyond some threshold.

Cash Collateral %, Derivatives: Fraction of cash-collateral pledged over total collateral pledged

for derivative transactions.

PPE Collateral %, Derivatives: Fraction of PPE-collateral pledged over total collateral pledged

for derivative transactions.

Cash Collateral %, Debt: Fraction of cash-collateral pledged over total collateral pledged for

debt.

PPE Collateral %, Debt: Fraction of PPE-collateral pledged over total collateral pledged for

debt.

Appendix A2: Text-search Algorithm Description

Text-search Algorithm Description: Collateral for Financial Debt Contracts

Some examples of excerpts are disclosed in what follows: The following excerpt is from AMD’s 10-K,

fiscal year 2008:

On November 1, 2006, Spansion LLC entered into a new senior secured term loan facility

with a certain domestic financial institution, as administrative agent, and the lenders

party thereto, in the aggregate amount of $500 million... In connection with the senior

secured term loan facility, the Company and each of Spansion LLC, STI, Spansion

International and Cerium, collectively referred to as the loan parties, executed a pledge

and security agreement pursuant to which the administrative agent received a first

priority security interest in (a) all present and future capital stock of each

of the Company’s present and future direct and indirect subsidiaries,...

(b) all present and future debt of each loan party, but excluding certain

intercompany debt to a foreign subsidiary, (c) all present and future other

property and assets of each loan party, but excluding intellectual property

and any equipment subject to a lien securing a capitalized lease permitted

by the credit agreement for the senior secured term loan facility, and (d)

all proceeds and products of the property and assets described above. The

net book value of the pledged assets as of December 31, 2006 was approximately $663.5

million.”

The following excerpt is from AK Steel Inc. 10-K, fiscal year 2013:

“AK Steel Holding Corporation announced today that its subsidiary, AK Steel Corpo-

ration, has successfully priced a private offering of $30.0 million aggregate principal

amount of its 8.750% senior secured notes due 2018 , which were offered as an add-

on to its outstanding $350.0 million aggregate principal amount of 8.750% senior

secured notes due 2018. The add-on notes will be fully and unconditionally guaran-

teed on a senior basis by AK Holding and will be secured by substantially all real

property, plant and equipment of AK Steel and proceeds thereof. ”

The following excerpt is from Alon USA Energy Inc. 10-K, fiscal year 2012:

“The Alon Energy Term Loan is secured by a second lien on cash, accounts re-

ceivable and inventory and a first lien on most of our remaining assets....

We have a $240.0 million revolving credit facility... The Alon USA LP Credit Facility

is secured by (i) a first lien on our cash, accounts receivables, inventories

and related assets and (ii) a second lien on our fixed assets and other

specified property,... The Paramount Credit Facility is primarily secured by (i)

a first lien on cash, accounts receivables, inventories and related assets

and (ii) a second lien on Alon Holdings’ fixed assets and other specified

property. In October 2009, Alon Refining Krotz Springs, Inc. issued 13.50% senior

secured notes in aggregate principal amount of $216.5 million in a private offering.

In February 2010, ARKS exchanged $216.5 million of Senior Secured Notes for an

equivalent amount of Senior Secured Notes registered under the Securities Act of

1933. ...The terms of the Senior Secured Notes are governed by an indenture and the

obligations under the Indenture are secured by a first priority lien on ARKS’

property, plant and equipment and a second priority lien on ARKS’ cash,

accounts receivable and inventory.”

Table A2: Text-search Procedure: Collateral for financial debt. The table below summa-rizes the data extraction process for the sources of collateral pledged in financial debt contracts.The procedure is simple as firms disclose this information in a relatively standardized manner. Wefirst run Step 1 and find candidate sentences. Then, we look in the (left-hand side) neighborhoodof the keyword in Step 1 to identify the type of debt contract (Step 2). Then, we can identify thesources of collateral pledged by looking at the (right-hand side) neighborhood of the keyword inStep 1 until the end of the sentence. Finally, in Step 4 we get rid of the exclusions.

Step 1: Pre-selection of Candidate Sentences Step 2: Find Debt ContractKeywords Keywords

collateral a) Bank Debt:secured by revolving creditsecurity interest in credit lineguaranteed by line of creditcollateralized by credit facilit

term loanloanb) Public Debt or Private Placements:bondnote

Step 3: Identify Type of Collateral PledgedType Keywords

Tangible Assets propertplantequipmentlandmachinereal estatecapital stockfixturetangible (asset)fixed assetreserves

Intangible Assets intellectual propertintangible (asset)royaltpatenttrademark

Accounts Receivables receivableInventory inventorCash and Marketable Securities cash

marketablesecuritiessecurit

All Assets assetsall (ANY TEXT) assets

Step 4: Rule out exclusions

Text-search Algorithm Description: Collateral for Derivative Contracts

In the case of of derivative contracts, we identify the sources of collateral, the position for derivatives

(pledged vs. received) and the value of the collateral. The procedure consists of three sequential

search strategies. First, we search all the annual reports (10-K filings) looking for specific standard-

izes sentence structures used to disclose collateral requirements in derivative transactions. Second,

we limit the search to the section of the annual reports in which they discuss derivative transactions

and hedging. In that section, we use a less strict search procedure and we look explicitly for the

keyword “collateral”. The last step, is to do a search over the tables in the annual report for the

keyword “collateral” in order to make sure that the information regarding collateral for derivatives

is not contained in a table as opposed to in the text of the annual report. If strategy 1 achieves the

goal of locating and gathering collateral requirements well, we do not pursue search strategies 2 and

3. This strategy is addressed to minimize the existence of false positives.

The following excerpts are part of the first search strategy:

“10-K/1001/1001039/0000950148-03-002883.txt As of September 30, 2003, counterpar-

ties had pledged a total of $49 million of cash collateral.”

“10-K/1376/1376227/0001144204-14-012472.txt At December 31, 2013, UNG’s coun-

terparty posted $0 in cash and $0 in securities as collateral with the Custodian, as

compared to $1,700,020 in cash and $0 in securities at December 31, 2012.”

“10-K/0873/0873860/0001019056-11-000277.txt In addition, we were required to deposit

$18,684 of cash collateral with the counterparties to interest rate swap agreements we

entered into during the second quarter of 2010. The balance at December 31, 2010

includes $18,684 of cash collateral held by the counterparties to certain of our interest

rate swap agreements.”

“10-K/1115/1115836/0001104659-11-010333.txt As of December 31, 2010, OEH has

posted cash collateral of $1,558,000 (2009-$3,710,000) with certain of its derivative

counterparties in respect of these net liability positions.”

The following table explains the procedure in detail.

Tab

leA

2:

Text-

searc

hP

roced

ure

:C

ollate

ral

for

deri

vati

ves.

Th

eta

ble

bel

owsu

mm

ari

zes

the

data

extr

act

ion

pro

cess

for

the

am

ou

nt,

sou

rces

an

dd

irec

tion

of

coll

ater

al

(ple

dge/

rece

ive)

ind

eriv

ativ

eco

ntr

acts

.T

he

pro

ced

ure

isre

lati

vely

sim

ple

as

firm

sd

iscl

ose

this

info

rmati

on

ina

rela

tive

lyst

and

ard

ized

man

ner

.W

efo

llow

3d

iffer

ent

stra

tegi

esto

gath

erth

ed

ata.

Fir

st,

we

use

pre

-sp

ecifi

edca

nd

idate

sente

nce

sin

all

the

an

nu

alre

por

t.S

econ

d,

we

focu

son

the

der

ivat

ives

sect

ion

ofth

ean

nu

alre

por

tan

dse

arch

for

spec

ific

keyw

ord

sth

at

all

owu

sto

iden

tify

coll

ate

ral

ple

dged

/re

ceiv

ed.

Fin

ally

,w

ese

arc

hfo

ra

spec

ific

keyw

ord

inal

lth

eT

able

sin

the

annu

alre

port

.

Searc

hS

trate

gy

1:

Sente

nce

stru

ctu

re+

keyw

ord

inall

an

nu

al

rep

ort

(10-K

filin

g)

Ste

p1:

Iden

tify

loca

tion

Ste

p2:

Iden

tify

Ple

dged

/Rec

eive

dS

tep

3:

Iden

tify

Sou

rce

Ste

p4:

Extr

act

am

ou

nt/

un

its

Key

word

sK

eyw

ord

sK

eyw

ord

sK

eyw

ord

s

cou

nte

rpar

th

eld

+to

un

spec

ified

$co

llat

eral

hel

d+

AN

YT

EX

Tca

shm

illi

onh

eld

+by

mar

keta

ble

secu

riti

eshu

nd

red

thou

san

dp

ost

+by

secu

riti

esb

illion

pro

vid

e+

by

lett

erof

cred

itre

ceiv

e+

AN

YT

EX

Tp

rop

ert

rece

ive

+by

equ

ipm

ent

rece

ive

+to

real

esta

tere

ceiv

e+

from

rece

ive

+w

ith

tran

sfer

+w

ith

tran

sfer

+by

tran

sfer

+A

NY

TE

XT

sen

d+

by

Pre

-sel

ecte

dca

ndid

ate

sen

ten

ces:

”as

of

(mon

th)

(day

)(y

ear)

,[.

..]

+co

unte

rpar

t/co

llat

eral

”or

”as

of(m

onth

)(d

ay)

(yea

r)an

d(p

revio

us

year)

,[.

..]

+co

unte

rpart

/co

llate

ral”

”at

(mon

th)

(day

)(y

ear)

,[.

..]

+co

unte

rpart

/co

llate

ral”

or”a

t(m

onth

)(d

ay)

(yea

r)an

d(p

revio

us

yea

r),

[...

]+

cou

nte

rpart

/co

llate

ral”

Searc

hS

trate

gy

2:

Keyw

ord

searc

hin

all

text

of

deri

vati

ves

secti

on

inan

nu

al

rep

ort

Ste

p1:

Iden

tify

loca

tion

of

text

Key

word

s

coll

ater

al

Th

ere

main

ing

gath

erin

gst

eps

are

an

alo

gou

sto

sear

chst

rate

gy1.

Searc

hS

trate

gy

3:

Tab

lese

arc

h+

keyw

ord

inT

ab

les

of

all

text

inan

nu

al

rep

ort

Ste

p1:

Iden

tify

loca

tion

of

tab

lean

dte

xt

Key

word

s

coll

ater

al

Fair value of Financial Instruments reporting table example.

Since November 2007, we can further find information on collateral requirements in the fair value

table of financial instruments due to sfas 157. The above table is an example for the table in which

collateral for derivatives can be found in the 10-K filings.

Appendix A3: Disclosure in SEC’s Annual Reports (10-K filings)

The regulatory environment is key to understand what are the implications of disclosure require-

ments over time and the extent to which data can be gathered from the 10-K filings in the SEC’s

EDGAR system. There are two data requirements that we cannot derive with the text-search al-

gorithm. First, there is no mandatory disclosure for notional amounts since Sfas.105 (FASB 1990)

was superseded.43 Neither Sfas.119 (FASB 1994) nor Sfas.133 (FASB 1998) considered whether

the notional amounts had to be disclosed or not.44 As a result, only the extensive margin can be

identified (a hedging dummy). Second, only the approximate valuation of collateral pledged for

financial debt can be computed. Regarding the disclosure requirements for collateral in derivative

transactions, as far back in time as in 1990 under Sfas.105 all entities were required to disclose their

policy for collateral held/provided. In 1998, with the introduction of Sfas.133 (effective in 2003) the

requirement was ruled out and the situation was not reverted until 2008 when Sfas.157 and Sfas.159

were issued. Although the disclosure of collateral policy has been industry practice also during the

2000’s, the variables computed for derivatives collateral could be subject to measurement error, as

some firms may not report the information on the total collateral pledged/received.

43“Disclosure of Information about Financial Instruments with Off-Balance-Sheet Risk and Financial Instrumentswith Concentrations of Credit Risks”.

44“Disclosure about derivative financial instruments and fair value of financial Instruments” and “Accounting forDerivative Instruments and Hedging Activities”, respectively.

Appendix A4: Previous Findings on Collateral for Derivatives in the Literature

The first step in our empirical strategy is to build on existing work by Rampini et al. (2014). The

authors build a theoretical model in which firms face a trade-off between financing and risk man-

agement decisions, as both promises have to be collateralized. They operate under the assumption

that PPE is the source of collateral for both derivatives and financial debt. They conclude that

financially constrained firms prioritize collateral for investment purposes and thus, they hedge less

or do not hedge at all. Then, they take the predictions of the model to an empirical setup and test

the relation between the intensive margin of hedging and credit quality to find a positive relation

and show how firms reduce hedging around distress. However, they use no explicit data on collateral

for derivatives and debt for the analysis.

Therefore, we begin the analysis by gathering collateral pledged/received, for financial debt and

derivatives contracts, for the 23 airline companies included in Rampini et al. (2014). We compile

an augmented sample of the sample presented in Table 2 of Rampini et al. (2014). In addition to

the information of the fraction of next year’s fuel cost hedged provided by Rampini et al. (2014),

we add information on collateral requirements. Table A4 in the Appendix presents the summary

statistics for the sources of collateral in debt contracts and derivative transactions for the 23 airlines

in Rampini et al. (2014).

The results suggest that around 66.1% of the sample hedges fuel price volatility through deriva-

tive instruments, while about 83.3% of the sample relies on collateralized debt. Comparing these

results to the results for the entire sample in Table 1, we observe that the airlines industry reports

an above-average reliance on derivative instruments for hedging purposes (66.1% versus 29.8%).

Moreover, these firms are more likely than the average Compustat firm to rely on collateralized

financing (83.3% versus 55.8%).

Focusing on the sources of collateral for derivatives, 95.3% of the dollar value of collateral in

derivatives is cash for derivatives, while only 2,2% is PPE.45 For secured debt contracts, 67% of the

collateral pledged is PPE, while 12.2% is cash and marketable securities. Several conclusions can

be highlighted. First, cash is the main source of collateral for derivatives instruments also for the

airlines sample. Similarly, PPE is the main source of collateral for debt contracts for non-financial

corporations. These results do not seem to provide supporting evidence for the financing versus risk

management trade-off in Rampini et al. (2014). According to the results, the sources of collateral

for derivatives are different from those for debt contracts.

45The remaining 2.5% corresponds to letters of credit.

Appendix A4: Augmented RSV Sample. This table presents summary statistics at the airline-year level for the 23 airlines in the Rampini et al. (2014) sample. We report their complete Table 2 (I)Table 2 in RSV ) and we augment it with hand-collected data on collateral requirements for derivativetransactions (II) Collateral Derivatives) and data on collateral requirements for financial debt (III)Collateral Financial Debt). All variables in II) and III) are dummy variables or percentages andhave been created through manual gathering of data. Appendix A1 shows how Compustat variableshave been constructed. Appendix A2 describes those variables created through manual gatheringof data or through the text-search algorithm.

I) Table 2 in RSVN Mean Std Dev p10 p50 p90

% of Next year’s fuel expense hedged 244 0.381 0.388 0.000 0.240 1.000% of Next year’s fuel expense hedged 184 0.199 0.238 0.000 0.115 0.500for airlines without fuel pass throughFuel pass through agreement in place 270 0.222 0.417 0.000 0.000 1.000Fuel used, gallons 239 899 1038 29 367 2730Fuel Cost, per gallon 250 1.286 0.751 0.612 0.946 2.224Fuel Expense, Total 263 1056 1601 23 326 3034Fuel expense to Total Operating Expense 263 0.198 0.090 0.109 0.171 0.334Net Worth (bv) 270 0.458 2.837 -0.309 0.177 2.973Net Worth to Total Assets (bv) 265 0.189 0.291 -0.112 0.209 0.502Net Worth (mv) 260 1.583 2.574 0.032 0.531 4.830Net Worth to Total Assets (mv) 260 0.324 0.245 0.041 0.260 0.706Operating Income to Lagged Assets 260 0.118 0.136 -0.016 0.102 0.301

II) Collateral DerivativesN Mean Std Dev p10 p50 p90

Dummy Hedge 270 0.661 0.474 0.000 1.000 1.000Collateral Derivatives over Total Assets 270 0.116 0.647 0.000 0.000 0.000% Cash&Securities 0.953 0.130 0.643 1.000 1.000% Letter of Credit 0.025 0.100 0.000 0.000 0.000% PPE 0.022 0.089 0.000 0.000 0.000Cash&Securities 270 0.058 0.234 0.000 0.000 0.000Letter of Credit 270 0.004 0.060 0.000 0.000 0.000PPE 270 0.004 0.060 0.000 0.000 0.000Cash Collateral over Cash&MarketableSec 270 0.724 4.164 0.000 0.000 0.000

III) Collateral Financial DebtN Mean Std Dev p10 p50 p90

Dummy Secured Debt 270 0.833 0.373 0.000 1.000 1.000Collateral Debt over Total Assets 270 0.342 0.582 0.000 0.060 0.000% Cash&Securities 0.122 0.307 0.000 0.000 1.000% Letter of Credit 0.000 0.006 0.000 0.000 0.000% Receivables&Inventories 0.022 0.083 0.000 0.000 0.063% PPE 0.670 0.440 0.000 0.952 1.000% Unspecified 0.018 0.132 0.000 0.000 0.000Cash&Securities 270 0.221 0.416 0.000 0.000 1.000Letter of Credit 270 0.004 0.060 0.000 0.000 0.000Receivables&Inventories 270 0.076 0.266 0.000 0.000 0.000PPE 270 0.438 0.497 0.000 0.000 1.000Unspecified 270 0.025 0.158 0.000 0.000 0.000Cash Collateral over Cash&MarketableSec 270 4.123 16.381 0.000 0.000 7.116

Tab

leA

5:

Identi

ficati

on

,C

on

dit

ion

al

Ind

ep

en

den

ce

Ass

um

pti

on

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his

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port

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son

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mary

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stic

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for

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act

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tics

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ent

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ent

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ers

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state

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