Financial globalization and its effectspubdocs.worldbank.org/en/170881478491131381/Malaysia-2... · Financial globalization and its effects 1 Kuala Lumpur 2016 - Luis Servén. Plan

1

Financial globalization and its effects

Kuala Lumpur 2016 - Luis Servén1

Plan

• Gross capital flows

• Global factors and capital flows

• Global imbalances

• The gains from financial globalization


Gross capital flows.

Traditional focus has been on net flows (= current

account deficits).

But that may miss much of the action:

• The massive increase in gross asset positions in the

globalization period reflects booming gross, not net, flows

• Flows from resident and non-resident investors likely reflect

different factors – e.g., different risks or constraints facing

investors – which get muddled when looking at net flows

• Gross inflows pose stability risks different from those of

saving-investment gaps – related to leverage and the size of

the banking system


It is more informative to look separately at inflows and

outflows (Broner et al 2013)

• Capital inflows by non-residents [CIF]: net increase

in liabilities to foreign residents

• Capital outflows by residents [COD]: net increase

in foreign assets of domestic residents

[Notice: these are commonly labeled ‘gross flows’ –

but in reality they are net measures]

Net flows = CIF – COD ( = current account deficit)

Total gross flows = CIF + COD



05

10

15

20

25

30

05

10

15

20

25

30

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

Advanced Economies: capital inflows and outflows


-30

36

912

-30

36

912

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

Emerging Economies: capital inflows and outflows


-6-3

03

69

12

-6-3

03

69

12

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

Developing Economies: capital inflows and outflows

Gross vs net capital flows

This figure shows ellipses that account for the joint distribution of capital flows by foreign and domestic agents. One ellipse for each decade isreported. Each ellipse captures 103 points and each one point represent the average for that decade for a country in the sample.

Globalization

Retrenchment

Net Inflows

Net Outflows


In developing countries, big drop in gross flows over 1970s-80s.

Since 1980, big rise in gross flows (especially in rich countries – whose net

flows have fallen!)

Gross flows are much more volatile than net flows Kuala Lumpur 2016 - Luis Servén9

How do inflows and outflows vary over the cycle?

• Gross flows are strongly procyclical: in good

times, both CIF and COD rise significantly.

• CIF more procyclical in developing countries;

COD in industrial countries (but the difference is

modest)

• Strong (and increasing) positive correlation

between CIF and COD -- that’s why net flows

are less volatile than gross flows


Correlation between gross inflows and gross outflows


Advanced economies: capital inflows and outflows

-50

510

15

-50

510

15

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

United States

-50

510

-50

510

1980 1990 2000 2010

% o

f G

DP

Year

Japan

-10

010

20

30

-10

010

20

30

1980 1990 2000 2010

% o

f G

DP

Year

Germany

-50

050

10

0

-50

050

10

0

1980 1990 2000 2010

% o

f G

DP

Year

United Kingdom

-10

010

20

30

-10

010

20

30

1980 1990 2000 2010

% o

f G

DP

Year

France

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Italy

05

10

15

05

10

15

1980 1990 2000 2010

% o

f G

DP

Year

Canada

-10

010

20

30

-10

010

20

30

1980 1990 2000 2010

% o

f G

DP

Year

Spain

05

10

15

20

05

10

15

20

1980 1990 2000 2010

% o

f G

DP

Year

Australia


Emerging economies: capital inflows and outflows

-20

24

68

-20

24

68

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

India

-50

510

-50

510

1980 1990 2000 2010

% o

f G

DP

Year

Brazil

-50

510

15

20

-50

510

15

20

1980 1990 2000 2010

% o

f G

DP

Year

Mexico

-10

010

20

-10

010

20

1980 1990 2000 2010

% o

f G

DP

Year

Turkey

-50

510

15

20

-50

510

15

20

1980 1990 2000 2010

% o

f G

DP

Year

Thailand

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

South Africa

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Argentina

-10

010

20

30

-10

010

20

30

1980 1990 2000 2010

% o

f G

DP

Year

Chile

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Colombia

Developing economies: capital inflows and outflows

-50

510

15

-50

510

15

1980 1990 2000 2010

CIF

COD

% o

f G

DP

Year

Nigeria

-20

24

6

-20

24

6

1980 1990 2000 2010

% o

f G

DP

Year

Bangladesh

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Sri Lanka

-10

-50

510

-10

-50

510

1980 1990 2000 2010

% o

f G

DP

Year

Ecuador

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Tunisia

-50

510

-50

510

1980 1990 2000 2010

% o

f G

DP

Year

Dominican Republic

-50

510

15

-50

510

15

1980 1990 2000 2010

% o

f G

DP

Year

Ghana

-50

510

-50

510

1980 1990 2000 2010

% o

f G

DP

Year

Guatemala

-20

020

40

60

-20

020

40

60

1980 1990 2000 2010

% o

f G

DP

Year

Botswana

Financial turbulence (‘crisis’) is associated with a

retrenchment of gross flows in both directions

Both domestic and foreign investors reduce their flows

quite significantly – while net flows change much less



The pattern around the 2008 crisis is similar to other crisis: significant

decline in both CIF and CID.

Overall, domestic and global crises have qualitatively similar retrenchment

effects – but quantitatively bigger in the case of global crises.


Hence in turbulent times both foreign and investors

retrench (or repatriate their assets) – a two-way sudden

stop. In good times both expand.

This suggests the potential presence of shocks with

asymmetric effects on domestic and foreign investors:

• Asymmetric information on domestic vs foreign asset returns

• Differential sovereign and expropriation risk

• Financial constraints – e.g., forced deleveraging

• In contrast, symmetric shocks (e.g., TFP) are not the likely

driving force.

But the CIF-COD correlation may also reflect in part official

intervention to defend the exchange rate when CIF falls – a decline

in reserve accumulation represents a fall in COD.


Global factors and capital flows

There is growing evidence that gross capital inflows and

outflows, as well as the prices of risky assets around the

world, strongly reflect the action of common factors.

• Flows to different countries are highly correlated.

• Different kinds of flows are highly correlated too.

Portfolio debt and bank-related flows show the highest

commonality.

The commonality refers to gross capital flows – net flows show

much less comovement.


Source: Rey (2013) Kuala Lumpur 2016 - Luis Servén20

• How big is the role of common factors in capital flows?

• We can use a factor-model setting to quantify their contribution to gross inflows and outflows (Barrot and Servén 2016)

Large panel dataset: 82 countries, annual data for 1979-2014. IMF data on inflows, outflows and net flows.


Assessing common factors

Multi-level factor model

N countries, M regions / groups with Nm countries each; ∑ Nm= N

– Countries’ group membership known a priori (Ando and Bai 2015 for the unknown case)

– Necessarily arbitrary (e.g., North America; developing vs emerging)

Three types of factors:

– Global factors (rG) – affect all countries in the world

– Regional / group factors (rm) – affect all countries in a group

– Idiosyncratic (or domestic) factors – specific to a country



Basic equation:

𝑦𝑚,𝑖,𝑡 = 𝛾𝑚,𝑖 ′𝐺𝑡 + 𝜆𝑚,𝑖′𝐹𝑚,𝑡 + 𝑢𝑚,𝑖,𝑡

Collecting all countries, groups and years:

𝑌 = 𝐺Γ′ + 𝐹Λ′ + 𝑈

Y (TxN): capital flow measure (scaled by trend GDP). Each column is a country.G (TxrG): global factorsF (Tx∑rm): group factorsU (TxN): idiosyncratic factors (residual)

Γ, Λ: factor loadings – can vary freely across countries



• Factors are unobserved, so both factors and loadings need to be estimated. This requires identifying assumptions:

𝐺’𝐺

𝑇= 𝐼;

𝐹𝑚’𝐹𝑚

𝑇= 𝐼 for all m

𝐹𝑚’𝐺 = 0 for all m: global and group factors are mutually orthogonal

• This suffices to uniquely determine the factors (up to a sign change)– use the loading of a ‘major country’ to set the sign (e.g., USA, Brazil…)



• Classical PC estimation is straightforward in single-level models (Bai 2003), but not in multi-level models.

– Earlier applications of multi-level models typically use Bayesian methods

• Instead we use a recent extension of PC methods to multi-level models

– Breitung and Eickmeier 2015, Choi et al 2015, Wang 2014

• Important: we do not need to assume 𝐹𝑚’𝐹𝑛 = 0 for 𝑚 ≠𝑛, i.e., regional factors can be correlated across regions

– Big difference with earlier (Bayesian) literature imposing (wrongly?) orthogonality – leads to an over-identified model.



• ‘Sequential least squares’min𝐺,𝐹,Γ,Λ

𝑆𝑆𝑅 = 𝑡𝑟 (𝑌 − 𝐺Γ′ − 𝐹Λ′)′(𝑌 − 𝐺Γ′ − 𝐹Λ′)

1. Start from initial estimate of global factors (we use CCA)

2. Compute estimate of regional factors

3. Iterate until convergence

• Information criteria (ICp2,HQ,BIC) to determine the number of factors rG and rm

– In all cases one factor suffices.



The three global factors are strongly correlated.They display considerable persistenceThey are significantly correlated with the Chinn-Ito measure of financial openness

Figure 4 Global Factors

-2-1

01

23

4

-2-1

01

23

4

1975 1985 1995 2005 2015

CIF

COD

NET

Financial Openness

Glo

bal F

acto

rs

Year

• All three global factors correlate with global interest rates, risk spreads and (less robustly) global commodity prices

• The correlation with the financial openness measure is quite robust


1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Financial Openness 6.575*** 2.451*** 4.537** 2.457** 2.835*** 6.004*** 3.226*** 6.662*** 3.476*** 4.325*** 5.611*** 1.899** 3.741** 1.879** 2.983***

[4.452] [2.881] [2.372] [2.183] [3.698] [4.160] [4.527] [3.320] [3.082] [5.653] [2.998] [2.501] [2.543] [2.504] [3.317]

1st Lag Global factor 0.630*** 0.603*** 0.746*** 0.582*** 0.508*** 0.365*** 0.623*** 0.354* 0.781*** 0.740*** 0.880*** 0.608***

[6.068] [5.347] [8.417] [5.470] [3.940] [3.280] [4.971] [2.003] [7.866] [6.412] [10.07] [4.106]

Interest rate 0.0726* 0.0995** 0.0622**

[1.896] [2.181] [2.382]

Risk spread -0.423** -0.515** -0.270**

[-2.184] [-2.237] [-2.380]

Real Commodity price 0.00126 0.00386* 0.00388***

[1.175] [1.856] [2.792]

Constant -3.358*** -1.225*** -2.684** -0.248 -1.665*** -3.066*** -1.645*** -3.944*** -0.581 -2.961*** -2.865*** -0.958** -2.237** -0.320 -2.271***

[-5.063] [-3.120] [-2.409] [-0.735] [-3.925] [-4.312] [-4.410] [-3.262] [-1.012] [-4.057] [-3.180] [-2.551] [-2.649] [-0.856] [-3.813]

Observations 35 34 34 34 34 35 34 34 34 34 35 34 34 34 34

Adjusted R-squared 0.554 0.736 0.763 0.791 0.731 0.457 0.604 0.645 0.684 0.625 0.396 0.818 0.837 0.833 0.840

Q (4) 17.15 6.951 6.942 7.039 6.601 10.17 6.100 5.205 7.431 4.898 49.85 1.365 1.460 3.132 3.589

P-value 0.00181 0.139 0.139 0.134 0.159 0.0376 0.192 0.267 0.115 0.298 3.87e-10 0.850 0.834 0.536 0.465

t-statistics in brackets

*** p<0.01, ** p<0.05, * p<0.1

Dependent variableGlobal CIF Global COD Global NET

Table 4 Covariates of the Global Factors

Table 5 Correlation matrix of regional factors, 1979-2013

(a) Gross capital inflows by foreign agents (CIF)

(b) Gross capital outflows by domestic agents (COD)

North

America

Latin America

and Caribbean

Asia and

PacificEurope

Middle East

and North

Africa

Sub-

Saharan

Africa

North America 1.000

Latin America and Caribbean -0.583 1.000

Asia and Pacific -0.547 0.515 1.000

Europe 0.227 -0.065 -0.136 1.000

Middle East and North Africa -0.369 0.560 0.593 -0.048 1.000

Sub-Saharan Africa -0.426 0.502 0.183 -0.441 0.057 1.000

North

America

Latin America

and Caribbean

Asia and

PacificEurope

Middle East

and North

Africa

Sub-

Saharan

Africa

North America 1.000

Latin America and Caribbean -0.164 1.000

Asia and Pacific -0.160 0.409 1.000

Europe 0.199 0.199 0.180 1.000

Middle East and North Africa -0.070 -0.278 -0.054 -0.573 1.000

Sub-Saharan Africa 0.231 0.317 0.042 0.525 -0.536 1.000






Advanced Emerging Developing

Advanced 1.000

Emerging 0.512 1.000

Developing 0.090 0.561 1.000


Advanced 1.000


Developing 0.635 0.592 1.000






Advanced 1.000


Developing 0.090 0.561 1.000


Advanced 1.000


Developing 0.635 0.592 1.000

Variance decompositions

• The factor model permits decomposing the variance of capital flows into 3 orthogonal components –global, regional, domestic.

• Because regional factors can be correlated, we can further decompose each one of them into:

– A component orthogonal to all other regional factors: unambiguously associated with the home region (call it the ‘own region’ component)

– A component correlated with other regional factors: call it the ‘cross region’ component




Figure 6 Variance decomposition of Gross Inflows

(a) World Regions

0 20 40 60 80 100

World

Developing economies

Emerging economies

Advanced economies

Global share Cross-region share Own region share Country share

1979-2013

1990-2013

1979-2013

1990-2013

1979-2013

1990-2013

1979-2013

1990-2013

Variance decompositions

• On average, common factors (global + regional / group) contribute around 40-50% of the variance– There are no big changes over time (puzzling! Due to timing?)

• But the roles of the different components vary a lot across world regions– North America and Europe show the largest contribution of the

global factor and the smallest of the idiosyncratic factor – they are the most financially-integrated regions

– In Europe, the regional factor has become more important over time for CIF – at the expense of the global factor

– Latin America shows the biggest contribution of idiosyncratic factor and the smallest of the global factor



Kuala Lumpur 2016 - Luis Servén370 20 40 60 80 100

World

Developing economies

Emerging economies

Advanced economies

Global share Cross-region share Own region share Country share

1979-2013

1990-2013

1979-2013

1990-2013

1979-2013

1990-2013

1979-2013

1990-2013

Figure 7 Variance decomposition of Gross Outflows

(a) World Regions

• Risky asset prices across the world also reflect the action of global factors

• Miranda-Agrippino and Rey (2015)

– Commodity prices

– Corporate bond and stock price indices

Monthly data expressed in log-differences

• Two-level factor model, Bayesian estimation (so group factors are forced to be mutually orthogonal).



The global factor is strongly negatively correlated with measures of volatility



The global factor is also negatively correlated with measures of risk premia


Sovereign spreads likewise show a high degree of

comovement across countries – higher than capital flows and

other asset prices.

Longstaff et al (2011) analyze the role of local and global

factors in the CDS spreads of 26 countries over 2000-2010.

• First, compute principal components.

-- almost 2/3 of the variance of monthly CDS changes is accounted

for by the first principal component alone; 80 percent by the first 3.

-- commonality increases in the crisis years (2007-10): the first

principal component accounts for 75% of the variance

-- the first component is highly correlated (.61) with the VIX and the

US stock market return (-.74)

Commonality is weaker for equity returns: 46 percent accounted for

by the first principal component; 58 percent by the first 3.Kuala Lumpur 2016 - Luis Servén43


To understand what’s behind the commonality, regress spreads

on local and global variables and separate the respective

contribution of each group to the overall variance

• Similar to Albuquerque, Loayza and Servén with FDI (JIE 2005)

• Local variables: stock market return, exchange rate change, foreign

reserve accumulation

• Global variables:

• Returns: U.S. stock market excess return, changes in U.S. 5-year

Treasury yield, corporate and high-yield spreads.

• Risk premia: changes in ‘equity premium’ (P/E), volatility

premium (spread between implied and realized volatility), term

premium

• Investment flows: new flows into equity and bond mutual funds.

• Spreads of other countries (global and regional averages) Kuala Lumpur 2016 - Luis Servén45


47

How does international financial integration change the role of global factors?

Albuquerque, Loayza and Servén (2005) first assessed this question for FDI flows using a standard model

– Integration should bring the world closer to an equilibrium with perfect risk sharing

– Hence it should raise the relative importance of global factors:

• Local risk can be more easily traded away• In the extreme of complete markets and perfect risk-sharing

all factors would be global

48

Global and local factors• Example: 2 periods and 3 countries subject to productivity

(and endowment) shocks.– Technology shows decreasing returns – Consumers / investors are risk averse

Both forces drive FDI

There are restrictions on asset trade and FDI: – No trade in assets (other than FDI) – Country 1’s consumer can invest in 1, 2; – Country 3’s consumer can invest in 1, 3; – Country 2’s consumer does not invest anywhere.This offers illustrative examples of local and global factors.

49

Global and local factors

Country 1

Country 3

Country 2

– Productivity shocks to 2 orthogonal to 1&3 are local factors (only affect FDI into 2, not into 1)– Productivity shocks to 2 correlated with 1&3 are global factors (affect all FDI flows) – An endowment shock to 2 perfectly (negatively) correlated with the endowment shock to 1 is a local factor– As restrictions on asset trade and FDI are lifted, some local factors may disappear altogether (e.g., the endowment shock can be fully diversified by 1&2) or become global.

50

Empirically, financial integration would be reflected in:

– In a factor model setting, the variance contribution of the common factors should rise

– In a standard regression setting, the contribution of the global variables to the overall explanatory power should rise relative to that of the local variables

51

Implementation

• Local FDI determinants: variables affecting the perceived profitability and risk from investing in the host country:

– local productivity, taxes, macro volatility, expropriation risk…

• Global FDI determinants:

– Worldwide cost of capital (e.g., world interest rates)

– Global forces affecting the local variables -- e.g., world productivity shocks driving local ones [Glick-Rogoff 1995]

52

Empirical Analysis

• Global variables: taken from literature explaining cross-

section of equity returns

G3 interest rate

World stock market return

U.S. credit spread (global risk appetite)

World per capita GDP growth (global productivity)

U.S. yield curve slope (premium on long-term assets)

Add: G3 inflation rate to transform returns to real

terms (but leave its coefficient unconstrained)

53

Empirical Analysis• Local variables: taken from the empirical FDI literature

Per capita GDP growth (local productivity)

Public consumption/GDP (overall tax burden)

Financial depth

RER depreciation (wealth effects; Froot-Stein 1991)

Institutional quality (property rights / exprop. risk)

Trade openness (trade-FDI complementarity)

Volatility of GDP growth

Volatility of ToT

Volatility of RER

54

Empirical Analysis

Sample: annual data 1970-2000

Unbalanced panel with 1,900 observations

94 countries (20 industrial + 74 developing)

Combined and separate estimates

Basic specification:

parameter homogeneity across countries (relaxed later)

observable global factors (relaxed later)

tj

L

tj

LG

t

G

jtj

tj uGDP

FDI FηFη ''0

55

Variable

All

countries

Industrial

countries

Developing

countries

Global Variables

G3 average interest rate -0.1304 ** -0.0928 ** -0.1368 **

0.0286 0.0467 0.0354

World stock market return -0.0019 -0.0008 -0.0022

0.0038 0.0038 0.0050

U.S. yield curve slope -0.3125 ** -0.2612 ** -0.3095 **

0.0518 0.0815 0.0615

U.S. credit spread 0.0106 0.2917 -0.1165

0.1956 0.3614 0.2474

G3 average inflation -0.0570 ** -0.0853 -0.0504

0.0270 0.0665 0.0335

World growth -0.1118 ** -0.1827 ** -0.1062 **

0.0371 0.0731 0.0457

Table 3. Determinants of FDI. Basic Model

56 SSEF – Sept 2009 © Luis Servén

Variable

All

countries

Industrial

countries

Developing

countries

Local Variables

GDP growth 0.0354 ** 0.0653 ** 0.0325 **

0.0135 0.0250 0.0144

Trade openness 0.0099 ** 0.0172 * 0.0090 **

0.0027 0.0096 0.0028

Financial depth 0.0055 * -0.0027 0.0114 **

0.0031 0.0045 0.0047

Government consumption / GDP -0.0716 ** -0.1975 ** -0.0645 **

0.0227 0.0579 0.0249

REER growth -0.0009 -0.0091 0.0000

0.0038 0.0068 0.0042

Institutional quality 0.0012 0.0082 -0.0011

0.0030 0.0055 0.0035

REER volatility -0.0112 -0.0052 -0.0107

0.0071 0.0149 0.0077

GDP growth volatility -0.0933 ** -0.0541 -0.0900 **

0.0204 0.0625 0.0210

ToT volatility -0.0002 0.0267 0.0006

0.0109 0.0204 0.0112

# Observations 1926 482 1444

# Countries 94 20 74

R-squared total 0.4482 0.3921 0.4529

R-squared within 0.1382 0.2303 0.1309

Table 3 (cont). Determinants of FDI. Basic Model

57

Empirical Analysis

Higher returns on international assets reduce FDI

(world growth and interest rates; term premium)

Higher local returns raise FDI (local productivity

growth, low tax burdens)

Higher local volatility reduces FDI (volatility of

productivity growth)

58

A Measure of Globalization

• Definition: Share of explained variance of FDI accounted for by global factors

– Identification problem: local and global factors are not mutually orthogonal

– Solution: attribute the covariance to the global factors --only the orthogonal component of the local factors is truly local (standard assumption but not critical for the results).

– Global factors affect FDI both directly and indirectly (i.e., through their impact on local factors)

59


Calculation:

• Construct the projection of local on global factors

10;

)'ˆ'ˆ(

)'ˆ(12

LLGG

GG

FFVar

FVar

)'ˆ(

)'ˆ,'ˆ(GG

LLGG

FVar

FFCov

• The globalization measure is computed as

60


• Implementation: based on parameter estimates (ignoring country fixed effects) from rolling regressions over a moving 16-year window

• Three results:

– Rising trend in globalization: from under 10 percent in 1985 to 60 percent in 2000.

– Globalization is higher for industrial than for developing countries – but the difference has narrowed lately (not significant after 1993)

– Trends in LAC and Asia are similar

61

Share of explained variance of FDI/GDP

accounted for by global factors

62

A Measure of Globalization • Where does the rising importance of global factors come from? • Direct ( ) vs indirect effect: the direct effect dominates, but the

indirect effect also shows a rising trend.0

63

A Measure of Globalization • Where does the rising importance of global factors come

from – their increasing relative variability or their increasing impact on FDI ?

• Further decompose the change in the direct effect into change in slope parameters + change in variance ratio:

where

64


• Most of the effect comes from the rising regression coefficient on the global factors.

• The variance ratio plays a minimal role – if anything, it works in the opposite direction

65

Global Factors and Liberalization

• Next, check that opening up of capital markets helps explain the larger role of global factors in FDI– Opening up makes it easier to trade away unsystematic risk.

The role of local factors in FDI should decline.

– Deeper integration can turn local factors into global.

• Empirical analysis using indicators of financial openness:– Official liberalization, first sign, investability (from Bekaert et al)

– IMF: AREAR restrictions

• Simple regressions of globalization measure on (GDP-weighted average of) openness indicators

• Strong positive association in every case

66

(robust standard errors and regression R2 under each coefficient)

67

Extensions and robustness checks

• Three sets of extensions:– Additional variables (with limited sample coverage)– Alternative specifications of the global factors (b)– Alternative samples and assumptions on the regression

residual.

• Three parts in each extension:– Re-estimation of the FDI equation– Recalculation of the globalization measure– Regression of globalization on liberalization indicators

• Qualitative results do not change

68

69

70

In summary,

• FDI flows reflect an increasing role of global factors

• The higher relevance of global factors is associated with increased financial integration

• But local factors continue to be important– Those considered (growth, openness, government size, macro

volatility) account for 40% of explained FDI variance.

– Unmeasured local factors are likely behind the fixed effects

The growing role of common factors reveals a ‘global financial

cycle’ driven by a few key variables summarizing global

financial conditions (Rey 2013, Bruno and Shin 2014):

• monetary policy in the US

• risk perceptions (as summarized by e.g., VIX).

When monetary policy loosens, the VIX falls, bank leverage

rises and gross credit flows rise as well.


The global cycle reflects the key role of the U.S. dollar:

• Dollar credit extended to non financial borrowers outside the US is worth

approximately 13% of non US World GDP (McCauley et al. 2014)

• Dollar also widely used around the world by asset managers

• Top 10 global asset management firms have more than $19 trillion in assets under

management

• Important role for global banks, in particular EU based

• Dollar as a funding currency: monetary policy has a direct effect on interest

payments, cash flow and net worth

• Dollar as an investment currency: a change in discount rate has an effect on

valuation of dollar assets, which can be used as collateral



Global banks play a central role in the global financial cycle

(Bruno and Shin 2014)

The leverage cycle of global banks is the key transmission

mechanism of financial conditions across borders

• Setting: regional banks borrow from global banks to lend

to local borrowers facing a maturity mismatch



• The leverage of global banks drives cross-border bank flows –

and thus the ‘common factor’

• Also, US dollar appreciation is associated with financial

tightening

– Local borrowers become riskier due to their currency mismatch, so

banks cut back on lending

– Consistent with the fact that financial crises are associated with dollar

shortages

• In regression results, cross-border bank flows are significantly

affected by global leverage and USD RER changes



Miranda-Agrippino and Rey (2014): assess the role of the ‘global

credit channel’ in the transmission of U.S. monetary policy

They set up a VAR to examine the global effects of U.S. monetary policy.

An increase in the effective fed funds rate has strong effects on:

• the global component of asset prices (-)

• the risk premium (+)

• the volatility of asset prices (+)

• bank leverage in the US and the EU (-)

• global domestic credit (with or without US) and cross border credit (-)




US monetary policy

• affects credit spreads and risk premia globally

• affects leverage and credit flows internationally

Thus it drives, at least in part, the Global Financial Cycle

• Countries may import monetary and financial conditions (even asset price

bubbles!) which do not necessarily fit their economies.

• And they show this applies regardless of countries’ exchange rate regime –

i.e., to Canada, U.K., Sweden,…

So Mundell’s trilemma really is just a dilemma – whether to have

an open capital account or not


Global imbalances

Until the global crisis, financial globalization was accompanied

by a surge in the current account gaps of major economies

• Deficit: USA, EU periphery

• Surplus: China, Germany, Japan

and also oil exporters

The magnitude of the imbalances has declined post-crisis



Persistent global imbalances are hardly a new phenomenon

• Canada and Australia ran 7%+ CA deficits over 1870-

1913 (Taylor 2002)

• Since 1980, current account balances of G-20 countries

have exceeded +/- 4% of GDP 30% of the time

Source: Bracke et al (2008)

Two main novelties of the recent episode:

• Emerging markets (with China and oil exporters at the top)

lending to the center country (unlike in the 1980s – Japan

and the EU were the creditors)

Japan-bashing in the 1980s, vs China-bashing in the latest

episode

• Bigger scale: the U.S. deficits of the 2000s reached above

1.5 percent of global GDP, and exceeded 1 percent of

global GDP for a decade.






Where did the imbalances come from?

Changes in the CA balance identically reflect changes in

saving and investment.

These provide an accounting decomposition – not an

explanation.

The big changes relate mostly to saving

1. Trend decline in U.S. saving rate since late 1990s

2. Steady rise in China’s saving since 2000 – to exceed 50% of

GDP at present (even higher than its massive investment!)

3. Recovery in Emerging Asia’s saving after 1997-98 crisis

4. Oil (and commodity) boom of the 2000s – with only modest rise

in oil exporters’ investment

[2+3+4 = Bernanke’s (2005) “global saving glut”]



The roots of global imbalances: view from goods markets

• Consumption rise in the U.S.

– Favored by credit growth, financial innovation, and the wealth effect of the housing bubble

• Consumption restraint in China

– Growth model oriented to production, rather than consumption: low real wages, declining social expenditure

– Weak corporate governance that allows big (semi-public) firms to retain their profits

– Limited access to outside financing for private firms and households


Modeling global imbalances

Most theoretical explanations take an asset-marketperspective: imbalances ultimately arise from international asymmetries in the supply and demand for assets

• Insurance needs vs asset availability in emerging markets:

– Limited supply of safe assets in fast-growing EMs –savers tilt their portfolios to rich-country assets

– Excess precautionary saving in EMs with underdeveloped financial and social protection systems – driven by idiosyncratic risks

– Post-1997 precautionary demand in EMs for foreign assets to self-insure against aggregate risks (‘suddenstops’)


• Neo-mercantilist policies in support of real undervaluation to promote growth

• Positive externalities from tradable sector development

These forces lead to accumulation of large foreign assetstocks.

• Note that in some explanations the accumulation is driven bygovernment action – Ricardian equivalence must fail(otherwise the private sector could just undo it)

• An overview of alternative explanations is Serven and Nguyen (2013)

Asymmetries in asset supply (Caballero et al 2008)

Real and financial shocks with unequally-developed

financial markets across world regions.

One region (U, the U.S. mainly) can supply financial

assets to global savers, the other (R, developing countries)

cannot.

R experiences fast growth but adverse financial shocks

(i.e., the crises of the 1990s).

The model generates increasing K-flows to U, falling

world interest rates, and increasing importance of U assets

in global portfolios – as actually observed.

The 3-region of the model adds E (Europe) that can also

supply assets but experiences adverse growth shocks.

Sketch of the model:

Two key parameters in each region

δ = financial development.

g = growth rate

Endowment economies: each period they receive an endowment

(1- δ) Xt, which cannot be capitalized in assets.

Finite-lived consumers á la Blanchard – they die at rate θ. At time of

death, they consume all their wealth, so Ct = θ Wt.

Only one saving vehicle – trees yielding dividend δXt per period. Their

total value is Vt. Hence ΔVt = rtVt – δXt

X grows at rate g.

With these assumptions, asset supply (scaled by output) equals

Vt / Xt = δ /(r – g)

Saving is ΔWt = + (1 – δ) Xt + r Wt – θ Wt ( = non-pledgeable income

+ return on wealth – consumption)

Integrating and letting t →∞, we get long-run (scaled) asset demand:

Wt / Xt = (1 – δ) / (g + θ – r)

Equating asset demand and supply Wt = Vt yields the autarky rate

raut = g + δθ

[equivalently, this follows directly since Wt = Vt implies Wt = Xt / θ,

and thus r = DXt / Xt + δθ = g + δθ.]

The Metzler diagram: Autarky equilibrium

The key ingredients of the model are (1) only a fraction δ < 1 of the

present value of future output can be capitalized in assets, and this is

reflected in asset supply; and (2) δ also affects the demand for assets.

Financial integration between two regions U and R with different raut

leads to a world interest rate that lies in between them.

If the two regions differ only in their respective δ, the resulting r is

just

r = g + θ[δRxR + δU(1 – xR)]

where xR is region R’s share of total output.

The current account is just CA = Δ W – Δ V. Hence

CAU / XU = g (r – rUaut ) / [(r – g) (g + θ – r)]

So the region with high autarky rate runs a (permanent!) CA deficit,

the region with low autarky rate runs a permanent surplus.

At the initial r, there is excess world demand for assets, so r must fall.

At the lower r in the new world equilibrium, R’s demand for assets

exceeds its supply, and conversely in U. U runs a permanent current

account deficit, matched by a permanent flow of saving from R.

A decline in θR (a saving glut – here lower death rate means lower

consumption) has analogous effects.

NFA in U World interest rate

The effects are larger if gR > g

Consider now a three-region world (U, R, E). Assume a collapse in gE

followed by a fall in δR.

The two shocks reinforce each other.

NFA in U, E World interest rate

In this model capital flows out of the high-growth

economies (rather than into them) because of their limited

ability to generate assets for savers.

The model illustrates how the collapse of asset markets in

Japan and the emerging market crashes of the 1990s can

account for the observed pattern of global imbalances and

interest rates.

What could halt these trends ?

• A recovery in Europe / Japan.

• Financial deepening in emerging markets

• A decline in China’s (or in oil exporters’) saving

Asymmetries in asset demand

• Mendoza et al (2009): model of sustainable global

imbalances driven by asymmetric financial market

development.

The driving force is idiosyncratic risk that generates

precautionary saving. The economy with less-developed

markets has higher precautionary saving than the advanced

economy.

Setup with identical consumers (with U’’’>0). One (risky)

productive asset. Two types of idiosyncratic shocks:

endowment shocks and investment shocks. No aggregate

shocks (to sharpen the focus on idiosyncratic risk)

Financial market development is measured by the (source)

country-specific enforcement of financial contracts

(appropriability of returns) – it applies to all assets held by

residents, whether at home or abroad.

Under autarky, the country with lower enforcement has

higher precautionary saving and lower interest rate.

Financial integration of the two economies then makes the

less-developed one hold positive net foreign assets. The

more advanced country holds negative net foreign assets.

Moreover, the advanced economy holds a positive position

in the productive (risky) asset – i.e., it finances risky

foreign investment with foreign debt, and earns a return

differential (a risk premium).

Equilibrium when economy #2 is less financially developed than 1

If enforcement is source-based rather than residence-

based, in equilibrium the country with better enforcement

still is a net debtor – but now its position in risky assets is

negative too.

In summary, financial integration among economies with

different degrees of financial development generates

global imbalances:

• the imbalances are large for reasonable parameter values

• they develop gradually after financial integration –

disappear only in the very long run.

• they are larger the bigger the difference between the

degrees of financial development of the two economies.

Idiosyncratic unemployment risk another way for

precautionary saving to generate global imbalances.

(Carroll and Jeanne 2013)

Unemployment risk (really ‘retirement risk’: the

unemployed never work again) makes individuals hold

precautionary wealth for self-insurance.

The key parameter is the scope of ‘social insurance’: a

government-run unemployment or pay-as-you-go benefit

that provides (partial) insurance. The more generous social

insurance, the lower saving and the lower net foreign

assets.

Empirically, there is evidence that social public spending

has a negative effect on household saving.

Here an increase in growth lowers target precautionary

wealth and thus foreign assets.

But if it is accompanied by a simultaneous increase in

unemployment risk (e.g., sector-wise growth uncertainty)

then the effects are reversed: target precautionary wealth

rises, and NFA rises too.

In a two-country version where countries have different

levels of social insurance, the country with the more

generous system is a net debtor.

Catch-up by the less-generous country in terms of social

insurance eliminates global imbalances. But it also reduces

global saving and raises the real interest rate. This lowers

K/Y and the real wage – and is welfare-reducing in both

countries (except for the current generation of workers that

start receiving the social transfers).

These papers focus on asset demand driven by self-

insurance against idiosyncratic risk.

An alternative explanation focuses on aggregate risk:

foreign asset hoarding to self-insure against sudden stops.

The underlying reason is the lack of international risk-

sharing mechanisms to insure against global financial

turbulence.

In the recent global crisis, countries holding massive

foreign reserve war chests have done better than the rest.

This tends to encourage more reserve hoarding post-crisis.

The preceding explanations of global imbalances focus on the

role of risk for asset demand.

An alternative explanation stresses ‘new mercantilism’: foreign

asset hoarding to sustain undervalued exchange rates and

export-led development.

Undervaluation in essence amounts to postponing consumption

in order to grow faster, so it trades off a present consumption

loss for a future consumption gain (Korinek and Serven 2015).

Minimal setting:

2 intermediate goods, traded and nontraded. Both use

capital and labor; tradables are (broad) K-intensive

(think of industry vs services)

Final good is produced using both intermediates.

Small open economy; capital account closed to private

agents.

1

1

1

Intermediate goods sectors:

Tradables

Non-tradables

Final goods sector

: > Tradables are relatively capital-intensive

: by suitabl

T T T

N N N

Z

T K A L

N K A L

Z A T N

Assumption 1

Assumption 2 e choice of units, T NA A K

Tradables are intensive in capital that yields economy-wide

productivity gains – a positive externality.

The externality is ignored by private agents, so the private

return on capital R falls short of its social return A*:

The decentralized equilibrium yields too much consumption

and too little investment (and thus growth is too slow).

This creates a divergence between the growth rate (standard

AK-style) that results in the decentralized equilibrium (DE)

and the social optimum (SP).

*; 0 1R A

1/

1/

1 [ (1 )]

1 [ (1 * )] 1

DE

SP DE

R

A

The socially-optimal growth rate is higher – driven by faster

capital accumulation and productivity growth.

Subsidizing capital yields a first-best (has only a second-order

cost vs a first-order benefit). The optimal subsidy rate is

Equivalently, one could provide an investment tax credit, or a

production subsidy to the tradable sector.

But there may be targeting problems:

• selective subsidies favor rent-seeking and leakage (as in the EU

linen scam)

• major information requirements for government to know which

sectors / goods to subsidize

• WTO rules prohibit direct or indirect export promotion

* (1 ) *Ks A

A second-best alternative is to ‘outsource’ the targeting

problem: ‘lending’ to foreigners (= hoarding foreign assets)

who only purchase tradable goods.

This reduces the local availability of tradables and raises their

relative price (= RER) and thus the return on capital.

Hence investment and growth rise too.

This raises future consumption, but at the cost of present

consumption – static loss vs dynamic gain.

Note the importance of the closed capital account – otherwise

private agents can undo the government’s asset hoarding.

Under reasonable parameterizations the net welfare effect

is small and in most cases negative.

When is the welfare effect more likely positive?

-- the bigger the gap in sectoral capital intensities

-- the more patient consumers are

-- the higher the elasticity of intertemporal substitution

Note: if foreign asset hoarding is welfare-improving, then

foreign aid (= asset gifts from abroad) must be welfare-

reducing.

Likewise, resource windfalls must reduce welfare (the

‘resource curse’).

• Should we still worry about large current account gaps (= net

capital flows) in the globalization era (Obstfeld 2011) ?

• The data show two facts:

(i) Gross flows far exceed net flows (and both show little

correlation).

(ii) Changes in net foreign asset positions are dominated by

valuation effects, not by current account imbalances.

So is the current account still a policy-relevant magnitude?

However, in the long run, the level of net foreign asset

positions tracks the cumulative CA fairly closely.

• the U.S. appears to be the main exception (also Switzerland)

In other words, capital gains and losses on assets and

liabilities cannot systematically offset the external wealth

effects of the current account.

• Although the strength of this conclusion depends on the

quality of the (noisy!) valuation data…

Hence the current account remains a (rough) guide to

long-term external sustainability.



The gains from financial integration

In theory, integration should be welfare-enhancing – through two

channels:

1. Growth: financial integration allows access to capital at

lower cost. In capital-scarce countries, this speeds up

investment and income growth, and hence consumption.

• Possibly also: productivity: financial integration may bring

improvements in TFP – from new technologies, better organization

of production, better corporate governance, financial

development…(e.g., Bonfiglioli 2009)


2. Risk: financial integration allows idiosyncratic risk to be

diversified away. Consumers can stabilize their consumption

streams and raise their welfare.

• The downside: the economy may become more vulnerable to financial

turbulence and crises arising from world markets. Thus even if

idiosyncratic risk falls, overall consumption volatility might rise!

The theoretical presumption is that financial integration opens

new markets / allows new transactions (= removes distortions)

But with otherwise incomplete markets (= other distortions

remain), it is not necessarily welfare-increasing


Massive literature (mostly reduced-form empirics) attempting to

quantify the effects of integration on key outcome variables:

growth, volatility…[Kose et al (2009) is a good survey]

Few robust results.

Endogeneity – integration is not a random occurrence

Omitted variables – integration often happens along with other measures

Heterogeneity – structural features and initial conditions vary a lot across

countries…


Consider the capital scarcity effect: in a capital-scarce

economy opening up to capital flows lowers the cost of capital

to world levels.

In the neoclassical (Solow) benchmark model, this speeds up

convergence: the economy jumps to the steady-state k/l ratio

without having to sacrifice current consumption.

At the time of opening-up, there is a surge in capital inflows and

investment. Output growth must rise too.

But this is just a transitional effect: the growth acceleration

eventually tapers off (unless productivity growth is affected).


How big is the welfare gain for a small country?

In the Solow model, it depends only on the gap between the cost

of capital under financial autarky, and the world real interest rate

(the cost of capital under financial integration).

Gourinchas and Jeanne (2006), Henry (2007)…

Let R = (gross) cost of capital under autarky, given by the

marginal productivity of capital – declining over time

R* = (gross) cost of capital under financial integration

Along the autarky growth path, R gradually falls towards R*


The welfare gain depends only on the gap between the cost of

capital under financial autarky, and the world real interest rate

For a marginal increase in capital inflows (=opening up), the

equivalent consumption variation can be shown to equal

1

0

ln ( *)

where is the ratio of capital flows to consumption, and

(1 )

is the 'permanent' cost of capital under autarky

t

t

t

d C R R

R R

So everything depends on how quickly R would have

converged to R*


Equivalently, the result depends on how far k is from its long-

run value (= how scarce capital is before opening up).

With standard parameters, the welfare gain from the capital

scarcity effect is quite small – unless capital is very scarce.


But these calculations assume that the autarky R converges very

quickly to the world R* -- by using rates of convergence derived

from the canonical growth model, around 5-6% per year.

Observed rates of convergence are much slower – around 2%

per annum.

This means that in autarky R would remain above R* for a very

long time – which makes a big difference for the welfare gain:

The equivalent consumption variation then rises from 1.7% of

annual consumption (Gourinchas and Jeanne 2006) to 7% !


The capital scarcity effect is typically analyzed in a partial

equilibrium setting:

• Integration of the capital-scarce country(ies) has no effect on

the world interest rate – does this work for China?

• The role of risk is ignored

Taking instead a general equilibrium view changes the

conclusions (Coeurdacier, Rey and Winant 2015):

• The steady-state itself is modified by financial integration in

the presence of risk.

• The capital-scarce country may not gain the most


General equilibrium setting

• Integration through risk-free bond only (important!)

• Stochastic neoclassical framework with two production

economies

• An emerging (risky) country (5% volatility of productivity

shocks)

• A relatively safer developed country (2.5% volatility)

• Emerging country starts with 50% of the capital of developed

country.


Questions

• What is the growth impact of financial integration?

• What are the patterns of capital flows?

• How big are the gains from financial integration?

• Who benefits the most?





















Globalization and risk sharing

Under optimal international diversification with complete

markets, consumers in each country can fully diversify country-

specific risk.

Representative consumers in all countries would reach the same

portfolio allocation (in terms of wealth shares) – a sort of “world

mutual fund” whose assets are claims on world output

each country’s income flow is a constant fraction (given by its

share in the world mutual fund) of world income.


Each country’s consumption is proportional to world

consumption

All countries share the same consumption growth rate.

consumption growth is perfectly correlated across countries

[whatever the correlation of their respective GDP growth rates]

In reality consumption growth is only weakly correlated across

countries. It is much more highly correlated with local GDP

growth.

Have these correlations changed with financial globalization?



Consumption correlations appear to have risen for rich countries but not forthe rest. Even for rich countries they remain pretty low (under 0.5).


One popular way to test the null hypothesis of perfect risk-

sharing is by running the regression

If the slope coefficient is significantly different from zero, the

null is rejected – which is the most frequent result.

Many papers go on to take as a measure of risk-sharing (1–

minus) the slope coefficient – although there is no solid

theoretical basis for this interpretation (more on this later).

The usual strategy is to compare slope estimates across

country groups and time periods (e.g., Kose et al 2009).


Cross-sectional regressions


Regressions over rolling windows (group medians)


• Overall, the extent of ‘risk sharing’ (the slope coefficients)

does not seem higher in the 1990s-2000s than in the 1970s.

• There is some evidence of an upturn after the 1980s, but only

for advanced countries

Another popular exercise is to add financial integration FO to

the regression interacted with the output term:

Heuristically, the sign of the new coefficient should be negative.


Kose et al do this using various measures of de jure and de facto

integration.

The interacted term is significant only for rich countries –

whether integration is measured by external assets, liabilities or

their sum.

This suggests possible ‘threshold effects’:• Integration only helps if you are highly integrated (like OECD)

• Integration only helps with highly developed financial markets and

institutions (like OECD?)


The weakness of this approach is that once perfect risk sharing is

rejected, the slope coefficients of these regressions are

meaningless.

To derive an empirical measure of risk sharing one needs to

specify an insurance arrangement – as in e.g., Crucini (1999):

• Prior to any income realization, permanent-income consumers /

countries contribute a fraction of their income to an income pool

• After the realization, they get the same fraction of whatever the pool’s

total income turns out to be

But empirical implementations of this setting assume that all

agents contribute the same fraction of income – so it is no suited

for a situation with heterogeneous agents / countries.


The obvious solution is to extend Crucini’s framework to the

case of agents / countries engaging in different extents of risk-

sharing.

• Hevia and Servén (2016): different agents / countries may contribute

different fractions of their income to the pool

• Their respective contributions can be interpreted as their degree of

risk-sharing

• Now the growth of each agent’s consumption depends on the

innovations to the permanent income of all agents

• The weights on the various innovations are nonlinear functions of the

income shares contributed by all agents.

• This leads to a highly nonlinear empirical model – a system with

nonlinear parameter constraints across equations (in contrast with the

simple OLS regressions of earlier applications)


In this setting, we can estimate the degree of risk-sharing of each

of 50 countries that make up the ‘world economy’.

Two-step approach:

i. Estimate the innovations to each country’s permanent

income – using alternative specifications

ii. Estimate the risk-sharing coefficients -- which theoretically

should lie between 0 and 1 but are not restricted to do so


Three main results emerge:

1. The estimated degree of consumption risk-sharing is higher

in advanced countries

2. It has been on the rise since the 90s –but mainly in rich

countries, so the gap has widened

3. Countries with more open capital accounts exhibit higher

degrees of risk-sharing








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Financial globalization and its effectspubdocs.worldbank.org/en/170881478491131381/Malaysia-2... · Financial globalization and its effects 1 Kuala Lumpur 2016 - Luis Servén. Plan