Terrorism, stock market returns, and volatility: A di … › 2016 › 02 › ...Terrorism, stock market returns, and volatility: A di erence-in-di erence approach Eric Lenz and Brian

Terrorism, stock market returns, and volatility: A

difference-in-difference approach

Eric Lenz and Brian Toney

May 2, 2017

Abstract

The societal effects from terrorist attacks are well-known: the loss of life, destruction of property,

and the inspiration of fear. However, can these effects be seen in the markets through stock price

returns in different sectors of the economy? We examine a sample of 16 market indices in Turkey

from May 2013 through August 2016 to assess market vulnerability to terrorist attacks. The severe

terrorist attack in July 2015 and 30 days after serve as the treatment for our difference-in-difference

methodology. Our findings suggest that investors in Turkish markets looking to protect against the

negative effects from terrorism should invest in the insurance, real estate, and the wood, paper,

printing sectors as opposed to the transportation, chemical, petroleum, and plastic, and telecommu-

nications sectors. Our results complement previous research into investment diversification strategies

surrounding terrorist attacks.

I. Introduction

Terrorist attacks directly influence the behavior of stock markets (Chesney et al., 2011), but what are

the effects to different sectors of the market? Previous research into terrorism and financial markets

follow event study methodologies; however, these methodologies do not typically control for long-run

market effects. We approach the behavior of stock markets following a terrorist attack with a difference-

in-difference methodology to account for these long-run changes. We account for exogenous changes in

the U.S. stock market, stock market behavior during the Islamic holy month of Ramadan, and terrorist

motivations related to rainfall in two major Turkish cities. The sample selection of Turkish stock markets

is particularly useful due to the series of terrorist attacks that follow a severe terrorist attack in July,

2015. This supports our theory that terrorist attacks may influence more terrorist attacks in addition

to specific sectors of the Turkish market and suggest a long-run methodological approach.

Miguel et al. (2004) find a significant effect on income from rain in the previous period. The decline

in income from a prior drought can increase the probability of civil conflict. The use of rain as an

instrument related to conflict is well-known and has led to further research that identifies exogenous

instruments such as foreign interest rates and commodity terms of trade. However, rain may also have a

direct effect on the probability of conflict if the conflict type is dependent on certain weather conditions.

1

Civil conflict is defined as a conflict involving an organized rebel group opposing a government force,

but terrorist attacks have separate motivations to inspire fear and create chaos. If the motivation for

a terror attack involves killing many people, then it is possible that a terrorist will strike when many

people are around and available as targets. Our research suggests that terrorist attacks are unlikely to

occur on rainy days when people are not in transit, attending events, or generally out in public.

While some attention has been paid to climate and conflict in Gleditsch (2012); Hsiang and Burke

(2014), the analysis of short-run rainfall shocks have garned less attention. There is evidence to suggest

that short-run changes in weather have important effects on social conflict risk (Hsiang and Burke,

2014); however, a daily-pattern of rainfall has yet to be analyzed in the context of conflict or terrorist

attacks.

This research also contributes to finance literature related to market behavior surrounding specific

events. Seyyed et al. (2005) find significantly lower volatility in the Saudi Arabian stock market during

the moving, Islamic religious event of Ramadan. Cam (2008) use an event study approach to determine

the effect of the 9/11 terrorist attack on U.S. industry stock return series. Cam (2008) suggest that

“industries reacts unevenly to terrorism” - specifically the transportation, tourism, and leisure industries

show negative returns while water, military defence, and communications industries experience positive

returns1. However, Kollias et al. (2010) show selective reaction by markets in the U.K. and Greece to

terrorist attacks.

The finance research surrounding events such as terrorism also have psychological effects. Drakos

(2010) find a negative effect on daily stock market returns in 22 countries through a world CAPM;

however, this research also suggests a psychosocial effect through which terrorism affects stock markets.

Ahern (2012) further emphasizes the psychological aspects of terrorist attacks on foreigners affected by

terrorist attacks in their home countries2. The psychological state of consumers and producers is also

affected through other exogenous factors like

The empirical research into the effect of terrorism on financial markets typically follow a GARCH

approach (Balcilar et al., 2017; Cam, 2008; Chesney et al., 2011) and an event-study3 approach (Cam,

2008; G. Andrew and Rodolfo, 2010). More recently, (Halkos et al., 2017) pursue event study methods

with a 90 day estimation window and 3 day pre- and post-event window4 surrounding a terrorist attack.

Much of this research suggests or assumes a transitory effect from terrorist attacks; however, there is

evidence for long-run effects (Arif and Suleman, 2014) and permanent effects to the stock markets due

to terrorism (Eldor and Melnick, 2004). We account for long-term effects from terrorist attacks with a

difference-in-difference methodology and a treatment period including a series of terrorist attacks.

The difference-in-difference methodology can measure the effect of introducing a new innovation

1Cam (2008) also show little impact on U.S. industry returns from foreign terrorist attacks in Bali and Madrid.2Specifically this research suggests detrimental effects to trust, subjective well-being, and importance of creativity and

freedom for foreigners (Ahern, 2012). However, these negative effects do not correspond to the macroeconomy in whichlocal economic output and household income increase possibly through positive psychological effects and increased socialcapital (Ahern, 2012).

3The event-study methodology follows Brown and Warner (1985).4By comparison, Aksoy (2014) pursue 5 and 10 day post-event windows applied to the Turkish stock market. Arin et al.

(2008) also find more severe effects from terrorism in emerging markets which may apply to Turkey.

2

to a return series which is grouped by stock index. Wang et al. (2009) use a difference-in-difference

approach to determine the effect of introducing derivatives trading on return volatility in the Chinese

stock market. Xie and Mo (2014) also use a difference-in-difference approach, but to model the impact

of index futures on Chinese stock market return volatility. Xie and Mo (2014) construct an index of

Chinese stocks and determine the before-and-after effect of introducing the CSI 300 index futures. We

similarly construct an index of stocks, but in the Turkish stock market with each group representing a

different sector of the Turkish economy.

Recent research has found exchange rate volatility related to terrorist attacks (Balcilar et al., 2017),

so we adjust the return series with the Turkish Lira to U.S. dollar exchange rate. Chulia et al. (2009)

find that stock market volatility from a terrorist attack transmits from U.S. markets to Euro markets,

but the London and Madrid terrorist attacks in the Eurozone did not affect U.S. markets. Therefore,

we control for the U.S. Dow Jones index returns’ influence on sectors of the Turkish economy with the

reasonable expectation that terrorist attacks based in Turkey do not affect U.S. markets.

II. Data

We collect the daily closing index price for 16 Turkish indices and construct sector-specific log daily

returns. The daily closing price data consist of 705 observations in each index for the period of February

4th, 2014 to December 27th, 2016. This period is centered around a severe terrorist attack on July

20th, 2015 with the prior period set as a control period and the latter set as a treatment period. There

is a corresponding index i and day t in the U.S. Dow Jones for each index and day in the Turkish Stock

Exchange. From this panel of Turkish stock prices in U.S. dollars, we compute the main dependent

variable of daily log returns.

The Turkish stock price indices are also adjusted to U.S. dollars to account for exchange rate

depreciation from February 2014 to December 2016. The adjusted Turkish stock prices in U.S. dollar

correlate better to the U.S. Dow Jones stock price control variable than the unadjust Turkish index in

Turkish Lira. The adjustment removes bias from the U.S. market that influences the price of goods

and services in Turkey. We also find that the stock exchanges in Turkey and the U.S. are not open on

identical days, so we remove the price observations in period t and record the daily return in period t+1

as the log price at t+ 1 minus the log price at t− 1.

Table 1: Summary statistics

Obs Mean Std. Dev. Min Max

Turkish returns 11280 -0.000 0.019 -0.156 0.121Ramadan (Ram) 11280 0.087 0.281 0.000 1.000Terror period (T ) 11280 0.501 0.500 0.000 1.000

Terror w/ inst. (T ) 11280 0.498 0.498 0.000 1.000U.S. Dow Jones returns 11280 0.000 0.011 -0.128 0.092Coup 11280 0.004 0.065 0.000 1.000

Note: The dependent variable is the log daily return in decimal terms.

3

Table 1 shows the number of observations, mean, standard deviation, min and max for the returns,

treatment, and control variables. The Turkish and U.S. Dow Jones returns are both zero on average,

but the Turkish returns exhibit a larger standard deviation. Table 2 show the same statistics for only

the Turkish stock market returns organized by index.

Table 2: Summary statistics by sector

Obs Mean Std. Dev. Min Max

Banking 705 -0.000 0.023 -0.110 0.121Electricity 705 -0.000 0.021 -0.156 0.102Food and Beverage 705 -0.001 0.018 -0.084 0.066Real Estate Investment Trust 705 -0.000 0.019 -0.104 0.116Holdings and Investments 705 -0.000 0.018 -0.072 0.067Telecommunications 705 -0.001 0.019 -0.108 0.052Wood, Paper, and Printing 705 -0.001 0.017 -0.086 0.060Chemical, Petroleum, and Plastic 705 0.000 0.017 -0.065 0.058Insurance 705 -0.000 0.013 -0.051 0.054Wholesale and Retail Trade 705 -0.000 0.018 -0.080 0.061Tourism 705 -0.000 0.020 -0.125 0.087Top 100 index 705 -0.000 0.018 -0.085 0.084Services 705 -0.001 0.016 -0.082 0.064Transportation 705 -0.001 0.022 -0.114 0.092Financial 705 -0.000 0.020 -0.098 0.103Industrial 705 -0.000 0.016 -0.068 0.061Total 11280 -0.000 0.019 -0.156 0.121Note: There are 16 different sectors included in the sample of Turkish returns(including the Top 100 index).

Table 2 shows that the three sectors with the highest maximum returns are XBANK, XGMYO, and

XUMAL. The three sectors with the lowest minimum returns are XELKT, XTRZM, and XULAS. The

sectors with the greatest standard deviation from their mean are XBANK, XULAS, and XELKT. The

transportation sector, XULAS, varies the most from its mean and experiences the largest negative effects

from terrorist attacks. The real estate sector, XGMYO, has the second highest returns of all indices

and has a positive effect from terrorist attacks.

The timeline of Turkish attacks listed in the euronews article define the treatment period of terrorist

attacks. The terrorist attacks are determined through characteristics of terrorism in Rudy’s paper,

“The Definition of Terrorism” with the threshold of deaths set by the UCDP Armed Conflict Dataset

Codebook. The dummy variable, Terrori,t, denotes the period of terrorist attacks from July 20th, 2015

to December 27th, 2016. The period begins on the day of a severe terrorist attack and marks the start of

a series of terrorist attacks. The data setup is centered for the application of our difference-in-difference

methodology.

The interaction variable, Rami denotes the month-long, moving Islamic holiday from 2014 to 2016.

We control for this holiday because there is evidence that stock return volatility declines in Muslim

countries during Ramadan (Seyyed et al., 2005). The Ramadan control variable accounts for periods

4

of low volatility and less risk that may otherwise skew the effects from terrorist attacks in the Turkish

stock market.

The rainfall in two cities, Ankara and Istanbul, are instruments that determine the onset of a terrorist

attack. The rainfall in inches fallen in Ankara and Istanbul are added and squared to identify the average

rainfall in the two centers of productivity and terrorist attacks in Turkey. This rainfall variable determines

the probability of a terrorist attack which in turn alters the difference-in-difference estimator.

III. Methods

We apply a difference-in-difference methodology with GLS random-effects to obtain the effect of a series

of terrorist attacks in a treatment period on the returns of a representative sector (i) in the Turkish

economy. The main difference-in-difference model with a Ramadan interaction and rainfall instrument

is:

Tt = θ + δRainfallt + vt (1)

Ri,t = α+ β1Ii + β2Tt + β3Rami + β4(Ii × Tt) + β5(Ii ×Rami) + β6(Tt ×Rami) (2)

+ β7(Ii ×Rami × Tt) +Xω + εi,t

Equation 1 shows the first-stage estimation of the terror treatment variable, Tt, given rainfall in

Istanbul and Ankara, Rainfallt. We obtain the predicted value of the terror treatment variable, Tt,

and estimate the Turkish stock returns in the second-stage.

Equation 2 estimates the log daily Turkish stock return in index i at time t. Ii is a dummy

variable for the specific sector i (ex. transportation, telecommunications, etc.). Tt is the predicted

terror treatment variable from Equation 1 and begins after a severe terrorist attack on July 20th, 2015.

Rami is an index-specific dummy for Ramadan which is a Muslim religious holiday and is suggested to

influence stock return volatility. The difference-in-difference (DD) estimator, (Ii×Tt), is the interaction

between the index and treatment period. The difference-in-difference-in-difference (DDD) estimator,

(Ii ×Rami × Tt), is simply the interaction of the Ramadan dummy with the DD-estimator.

X is a vector of control variables containing index specific dummies for the coup attempt, the previous

day’s return, and the current period daily U.S. Dow Jones index return. The attempted Turkish coup is

represented by a dummy variable that begins on July 15, 2016 and lasts for 3 days. The Turkish index

return at time (t-1) is correlated to the index return at time (t). The U.S. Dow Jones index control

variable correponds to the 16 sectors of the Turkish economy and also accounts for the exogenous

infuence of U.S. markets on Turkish markets.

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IV. Results

Table 3 shows the highly-significant, negative effect of rainfall on the terrorist treatment variable, T ,

in Equation 1. Our theory suggests that rainfall lowers the probability of terrorist attack given that

terrorist motivations involve a large number of deaths and perhaps people do not travel or hold events

on rainy days. The rainfall data and dates of terrorist attack suggest that none of the 7 terrorist attacks

occur on a day with rainfall. Therefore, the negative coefficient in column (1) is expected and rainfall

is likely to lower the probability of terrorist attack.

Table 3: Prediction of terror treatment with rainfall instrument

Variables (1)

Rain -0.0828***(0.0291)

Constant 0.504***(0.00482)

Observations 11,280R-squared 0.001Notes: Estimation results for Equation 1:Tt = θ+δRainfallt+vt. Robust standarderrors in parentheses and *** p<0.01, **p<0.05, * p<0.1.

Table 4 shows the negative and positive effects from terrorist attacks in 6 sectors of the Turkish

economy during a treatment period (DD), with a rainfall instrument, and a Ramadan interaction (DDD).

The size and sign of the main estimators are robust to different model specifications; however, the

model fit improves with a Ramadan interaction (DDD) in the last column. The sectors facing negative

effects to daily returns from terrorist attacks are transportation, chemical, petroleum, and plastic, and

telecommunications sectors. The sectors experiencing positive effects to returns are insurance, real

estate, and wood, paper, and printing.

The coefficients in Table 4 show that the transportation index returns experience the most severe,

negative impact from terrorist attacks with an average coefficient value of -0.00150, while the wood,

paper, and printing index is the most positively-affected index with a coefficient value of 0.000130. The

sign of each coefficient on the difference-in-difference estimators generally follow the results of other

studies of terrorism and stock market returns Chesney et al. (2011). However, the insurance sector has

previously been negatively affected by terrorist attacks Chesney et al. (2011) and in Table 4 the effect

of terrorist attacks is positive. The explanation for this may lie in our indicator of terrorist attacks

which includes a range of observations and an increase in insurance premiums (Johnston and Nedelescu,

2006). In any case, we show in Table 4 that some Turkish stock indices benefit and others suffer from

terrorist attacks.

The comparison of coefficients between the 4 models in Table 4 show that the main difference-in-

difference estimators remain robust to different model specifications. Column (1) shows difference-in-

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Table 4: Main results

Main difference-in-differenceestimators for Index i

DD ModelDD Modelw/Instrument

DDD ModelDDD Modelw/Instrument

Transportation -0.00149*** -0.00150*** -0.00150*** -0.00151***Standard Error (0.000146) (0.000146) (0.000129) (0.000128)R2 0.0898 0.0898 0.0906 0.0906

Chemical, Petroleum, andPlastic

-0.00100*** -0.00102*** -0.00076*** -0.00077***

Standard Error (0.000157) (0.000157) (0.000149) (0.000148)R2 0.0893 0.0893 0.0901 0.0901

Telecommunications -0.00057*** -0.00049*** -0.00053*** -0.00044***Standard Error (0.000170) (0.000171) (0.000155) (0.000156)R2 0.0888 0.0889 0.0896 0.0896

Insurance 0.00130*** 0.00127*** 0.00113*** 0.00110***Standard Error (0.000154) (0.000155) (0.000142) (0.000144)R2 0.0888 0.0888 0.0896 0.0896

Real Estate 0.00036** 0.00034** 0.00050*** 0.00049***Standard Error (0.000173) (0.000174) (0.000157) (0.000158)R2 0.0894 0.0895 0.0902 0.0902

Wood, Paper, and Printing 0.00133*** 0.00137*** 0.00123*** 0.00128***Standard Error (0.000156) (0.000154) (0.000143) (0.000141)R2 0.0915 0.0916 0.0923 0.0923

Notes: Each column shows the 6 main difference-in-difference coefficient estimates on (Ii×Tt) corresponding to 4 differentmodel specifications. The columns from left to right show new coefficient estimates with added rainfall instruments andRamadan interactions. The standard errors and R2 values allow for a comparison of coefficients and models with each newinnovation. We estimate robust standard errors with *** p < 0.01, ** p < 0.05, * p < 0.1.

difference coefficients from equation 1 without a Ramadan interaction and column (2) shows the same

model, but with a rainfall instrument. In column (2), the rainfall instrument changes the coefficients

and r-squared only slightly, but the model fit slightly improves. Column (3) highlights the difference-in-

difference coefficents from equation 1 with a Ramadan interaction included in the model. The Ramadan

interaction in the DDD model improves the model fit (R2 increases from 0.0915 to 0.0923). Table 4

shows that the rainfall instrument and Ramadan interaction (DDD) improve the difference-in-difference

model and we further analyze the estimators interacted with a Ramadan dummy in Table 5.

Table 5 shows that by implementing Ramadan as an interaction variable in the difference-in-difference

(DDD) methodology, the model captures 12 affected indices affected by terrorism, compared to the five

indices captured by the original difference-in-difference model. Even with the addition of the Ramadan

variable, the statistically significant I(i, t) × T(i, t) coefficients from the original regressions remained

relatively unchanged. None of the five industries that shows effects in the DD model change signs or

became statistically insignificant when we implemented Ramadan as an additional interaction variable.

7

Table 5: Main effects during Ramadan

Main diff-in-diff-in-diff (1) (2) Main diff-in-diff-in-diff (3) (4)estimators for Index i DDD Model DDD w/ Instrument estimators for Index i DDD Model DDD w/ InstrumentTransportation -0.000809** -0.000847** Wood, Paper, and Printing 0.00097*** 0.00091**Standard error (0.000373) (0.000374) Standard error (0.000372) (0.000375)R2 0.0906 0.0906 R2 0.0923 0.0923

Chemical, Petroleum, and Plastic -0.00302*** -0.00301*** Financials 0.00180*** 0.00181***Standard error (0.000319) (0.000321) Standard error (0.000373) (0.000374)R2 0.0901 0.0901 R2 0.0906 0.0906

Wholesale & Retail Trade -0.000759** -0.000733* Banking 0.00267*** 0.00272***Standard error (0.000382) (0.000383) Standard error (0.000350) (0.00035)R2 0.0895 0.0895 R2 0.0896 0.0896

Tourism -0.00108*** -0.00110*** Insurance 0.00245*** 0.00249***Standard error (0.000391) (0.000392) Standard error (0.000352) (0.000352)R2 0.0909 0.0909 R2 0.0896 0.0896

Industrial -0.00082** -0.00078** Services 0.00062 0.00063*Standard error (0.000377) (0.000379) Standard error (0.000382) (0.000383)R2 0.0903 0.0903 R2 0.0899 0.09

Real Estate and Investment Trust -0.00180*** -0.00178***Standard error (0.000370) (0.000372)R2 0.0902 0.0902

Telecommunications -0.00127*** -0.00132***Standard error (0.000381) (0.000383)R2 0.0896 0.0896

Notes: Columns (1-4) show the coefficients on (Ii × Rami × Tt) and (Ii × Rami × Tt) in Equation 2 corresponding to12 different sectors. The standard errors and R2 values allow for a comparison of coefficients and models with each newinnovation. We estimate robust standard errors with *** p < 0.01, ** p < 0.05, * p < 0.1.

We also see a slight increase in the overall r-square value in the DDD regressions, which should be

expected as we add more regressors to the model. Therefore, with the implementation of Ramadan

as an additional interaction variable, we can see effects of terrorism in seven additional indices without

altering the results yielded in the DD model.

The results show that terrorism negatively affects the transportation, petroleum, wholesale & retail

trade, tourism, industrial, real estate, and telecommunication industries during Ramadan; while positively

affects the paper, banking, financial, insurance, and services industries. Terrorism negatively affects the

returns of chemical, petroleum, and plastic index the greatest during Ramadan with a coefficient value

of -0.302, while the banking industry has the greatest positive effect with a coefficient value of 0.267.

V. Discussion and conclusion

Increased terrorism may not affect the profitability of the banking industry because the clients of banks

may not be directly affected by terrorism, thus not affecting the profitability of the banking industries.

When there is an increase in terrorist activity, investors reallocate funds to other indices because they fear

that the confidence of consumers will decrease, which results in economic slowdown. This phenomenon

causes the demand for oil to decrease which leads to lower oil prices. How the market reacts to this

chain of events depends on the nature and location of the terrorist attack. If the attack took place

where it could jeopardize operations in the transportation or tourism sector, then you will see a decrease

in returns in that affected sector. The insurance market is unaffected from terrorism because people

8

could be overestimating the damage caused by an attack and the probability of having their capital

destroyed by an act of terror. This causes worried individuals to over-insure on their property, increasing

the revenue of the insurance companies. Terrorism negatively affects the telecommunications sector

because the Turkish government has started limiting internet access of its citizens after an act of

terrorism. This temporary prohibition from internet use causes firms in the telecommunications industry

to lose revenue, as their ability to sell advertisements decreases.

The wood, paper, and printing industry may be unaffected by terrorism for two reasons. First, many

of the companies in this index are located in areas that were unaffected by terrorism. For example,

International Paper, one of the largest paper producers in the country, has seven locations in Turkey.

(International Paper) None of the companys offices are located in cities that experience a terrorist attack,

thus the operations of the company was not directly affected by any of the seven attacks. Secondly,

wood and paper are inelastic goods, as in consumers consumption of these goods are not very sensitive

to the political and economic environment. It does not matter if a terrorist attack occurred; the need for

paper for schools and offices will stay about the same. In the case of real estate, we suspect that people

are migrating away from cities that experience a substantial amount of terrorism, such as Istanbul and

Ankara, and moving to safer areas. There is evidence suggesting that after the 9/11 attacks, people

vacated the NYC area in search of a safer place to live. (Viuker) However, those people need some

place to go. Since a majority of the attacks occurred in Ankara and Istanbul, perhaps people might be

moving from those high risk cities to cities that have not experienced as much terrorism.

9

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A Full regression results

Table 6: Ramadan and difference-in-difference interaction

(1) (2) (3) (4)

L.Turkish returns 0.0291∗∗∗ 0.0294∗∗∗ 0.0294∗∗∗ 0.0293∗∗∗

[0.00798] [0.00801] [0.00802] [0.00798]

U.S. Dow Jones returns 0.381∗∗∗ 0.390∗∗∗ 0.392∗∗∗ 0.395∗∗∗

[0.0465] [0.0517] [0.0516] [0.0500]

XU100 -0.000108[0.0000981]

Coup -0.0463∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00278] [0.00278] [0.00278] [0.00278]

Terror period (T) -0.0000704 -0.0000606 -0.0000704 -0.0000864[0.000156] [0.000156] [0.000156] [0.000156]

Ramadan (Ram) 0.00374∗∗∗ 0.00372∗∗∗ 0.00376∗∗∗ 0.00382∗∗∗

[0.000175] [0.000169] [0.000177] [0.000168]

Treat Ramadan -0.00389∗∗∗ -0.00405∗∗∗ -0.00390∗∗∗ -0.00384∗∗∗

[0.000385] [0.000354] [0.000388] [0.000386]

Ramadan XU100 0.000348∗∗

[0.000165]

DWJONES XU100 0.352∗∗∗

[0.0468]

DD XU100 -0.0000702[0.000160]

DDD XU100 0.0000400[0.000381]

XBANK -0.000101[0.0000984]

Ramadan XBANK 0.000643∗∗∗

[0.000161]

DWJONES XBANK 0.0418[0.0519]

DD XBANK -0.000283∗

[0.000159]

DDD XBANK 0.00278∗∗∗

[0.000351]

XHOLD 0.000176∗

[0.0000981]

Ramadan XHOLD 0.0000834[0.000168]

DWJONES XHOLD 0.0163[0.0519]

DD XHOLD -0.000106[0.000160]

DDD XHOLD 0.000200[0.000386]

XTCRT 0.000103[0.0000988]

Ramadan XTCRT -0.000863∗∗∗

[0.000159]

DWJONES XTCRT -0.0568[0.0500]

DD XTCRT 0.000122[0.000160]

DDD XTCRT -0.000676∗

[0.000382]

Constant -0.000467∗∗∗ -0.000462∗∗∗ -0.000479∗∗∗ -0.000473∗∗∗

[0.0000976] [0.0000977] [0.0000978] [0.0000984]

Observations 11264 11264 11264 11264# of indices 16 16 16 16R-squared within 0.0910 0.0896 0.0895 0.0896R-squared between 0.0203 0.0284 0.00596 0.0204R-squared overall 0.0910 0.0896 0.0895 0.0895Regression xtreg xtreg xtreg xtreg

Notes: This table is in the appendix.

12


(1) (2) (3) (4)


[0.00808] [0.00799] [0.00801] [0.00800]


[0.0519] [0.0475] [0.0500] [0.0483]

XTRZM 0.000329∗∗∗

[0.0000961]

Coup -0.0463∗∗∗ -0.0463∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00280] [0.00278] [0.00278] [0.00278]

Terror period (T) -0.0000831 -0.0000661 -0.000142 -0.0000683[0.000156] [0.000156] [0.000143] [0.000157]

Ramadan (Ram) 0.00375∗∗∗ 0.00373∗∗∗ 0.00379∗∗∗ 0.00381∗∗∗

[0.000178] [0.000173] [0.000175] [0.000168]

Treat Ramadan -0.00383∗∗∗ -0.00384∗∗∗ -0.00404∗∗∗ -0.00392∗∗∗

[0.000391] [0.000383] [0.000350] [0.000385]

Ramadan XTRZM 0.000158[0.000168]

DWJONES XTRZM -0.180∗∗∗

[0.0520]

DD XTRZM 0.000176[0.000160]

DDD XTRZM -0.00104∗∗∗

[0.000391]

XUSIN 0.000137[0.0000974]

Ramadan XUSIN 0.000555∗∗∗

[0.000161]

DWJONES XUSIN 0.266∗∗∗

[0.0478]

DD XUSIN -0.000147[0.000160]

DDD XUSIN -0.000720∗

[0.000378]

XSGRT -0.000646∗∗∗

[0.0000889]

Ramadan XSGRT -0.000544∗∗∗

[0.000166]

DWJONES XSGRT -0.0161[0.0501]

DD XSGRT 0.00104∗∗∗

[0.000143]

DDD XSGRT 0.00254∗∗∗

[0.000352]

XUHIZ -0.000242∗∗

[0.0000978]

Ramadan XUHIZ -0.000930∗∗∗

[0.000161]

DWJONES XUHIZ 0.187∗∗∗

[0.0486]

DD XUHIZ -0.0000486[0.000160]

DDD XUHIZ 0.000690∗

[0.000382]

Constant -0.000498∗∗∗ -0.000481∗∗∗ -0.000428∗∗∗ -0.000459∗∗∗

[0.0000961] [0.0000972] [0.0000863] [0.0000971]



13


(1) (2) (3) (4)


[0.00806] [0.00784] [0.00795] [0.00804]


[0.0478] [0.0501] [0.0492] [0.0449]

XGMYO -0.00000179[0.0000988]

Coup -0.0463∗∗∗ -0.0463∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00279] [0.00278] [0.00278] [0.00278]

Terror period (T) -0.000101 -0.0000760 -0.0000476 -0.000148[0.000153] [0.000157] [0.000151] [0.000137]

Ramadan (Ram) 0.00371∗∗∗ 0.00379∗∗∗ 0.00379∗∗∗ 0.00380∗∗∗

[0.000166] [0.000172] [0.000174] [0.000177]

Treat Ramadan -0.00380∗∗∗ -0.00388∗∗∗ -0.00383∗∗∗ -0.00402∗∗∗

[0.000372] [0.000391] [0.000386] [0.000375]

Ramadan XGMYO 0.000638∗∗∗

[0.000158]

DWJONES XGMYO 0.224∗∗∗

[0.0481]

DD XGMYO 0.000431∗∗∗

[0.000158]

DDD XGMYO -0.00173∗∗∗

[0.000370]

XGIDA -0.000308∗∗∗

[0.0000957]

Ramadan XGIDA -0.000431∗∗∗

[0.000166]

DWJONES XGIDA -0.117∗∗

[0.0501]

DD XGIDA -0.0000757[0.000161]

DDD XGIDA 0.0000475[0.000389]

XILTM -0.0000676[0.0000988]

Ramadan XILTM -0.000450∗∗∗

[0.000168]

DWJONES XILTM 0.0948∗

[0.0495]

DD XILTM -0.000495∗∗∗

[0.000155]

DDD XILTM -0.00127∗∗∗

[0.000382]

XKAGT -0.000569∗∗∗

[0.000101]

Ramadan XKAGT -0.000671∗∗∗

[0.000167]

DWJONES XKAGT -0.246∗∗∗

[0.0450]

DD XKAGT 0.00122∗∗∗

[0.000143]

DDD XKAGT 0.000971∗∗∗

[0.000372]

Constant -0.000470∗∗∗ -0.000446∗∗∗ -0.000463∗∗∗ -0.000446∗∗∗

[0.0000979] [0.0000953] [0.0000982] [0.0000989]



14


(1) (2) (3) (4)


[0.00801] [0.00801] [0.00812] [0.00799]


[0.0535] [0.0469] [0.0474] [0.0496]

XKMYA 0.000999∗∗∗

[0.0000690]

Coup -0.0463∗∗∗ -0.0463∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00278] [0.00279] [0.00277] [0.00279]

Terror period (T) -0.0000206 -0.0000738 0.0000191 -0.0000769[0.000141] [0.000156] [0.000129] [0.000156]

Ramadan (Ram) 0.00368∗∗∗ 0.00374∗∗∗ 0.00382∗∗∗ 0.00377∗∗∗

[0.000148] [0.000174] [0.000171] [0.000176]

Treat Ramadan -0.00368∗∗∗ -0.00397∗∗∗ -0.00382∗∗∗ -0.00392∗∗∗

[0.000325] [0.000376] [0.000373] [0.000386]

Ramadan XKMYA 0.00122∗∗∗

[0.000140]

DWJONES XKMYA -0.118∗∗

[0.0536]

DD XKMYA -0.000828∗∗∗

[0.000149]

DDD XKMYA -0.00296∗∗∗

[0.000319]

XUMAL -0.000158[0.0000978]

Ramadan XUMAL 0.000356∗∗

[0.000164]

DWJONES XUMAL 0.266∗∗∗

[0.0471]

DD XUMAL -0.0000539[0.000160]

DDD XUMAL 0.00186∗∗∗

[0.000373]

XULAS 0.0000134[0.0000979]

Ramadan XULAS -0.000961∗∗∗

[0.000159]

DWJONES XULAS 0.209∗∗∗

[0.0476]

DD XULAS -0.00157∗∗∗

[0.000129]

DDD XULAS -0.000790∗∗

[0.000373]

XELKT 0.000268∗∗∗

[0.0000960]

Ramadan XELKT -0.000112[0.000166]

DWJONES XELKT 0.0829∗

[0.0498]

DD XELKT 0.0000159[0.000160]

DDD XELKT 0.000329[0.000382]

Constant -0.000538∗∗∗ -0.000462∗∗∗ -0.000471∗∗∗ -0.000486∗∗∗

[0.0000693] [0.0000972] [0.0000977] [0.0000958]



15

Table 10: Ramadan interaction and rainfall instrument

(1) (2) (3) (4)


[0.00798] [0.00801] [0.00802] [0.00798]


[0.0465] [0.0517] [0.0516] [0.0500]

XU100 -0.000138[0.0000974]

Coup -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00278] [0.00279] [0.00278] [0.00279]

Terror w/ inst. (T ) -0.000128 -0.000118 -0.000127 -0.000143[0.000157] [0.000156] [0.000157] [0.000157]

Ramadan (Ram) 0.00371∗∗∗ 0.00369∗∗∗ 0.00373∗∗∗ 0.00379∗∗∗

[0.000173] [0.000168] [0.000175] [0.000166]

Treat Ramadan -0.00383∗∗∗ -0.00399∗∗∗ -0.00384∗∗∗ -0.00378∗∗∗

[0.000386] [0.000353] [0.000390] [0.000387]

Ramadan XU100 0.000377∗∗

[0.000163]

DWJONES XU100 0.352∗∗∗

[0.0468]

DD XU100 -0.0000133[0.000160]

DDD XU100 -0.0000169[0.000383]

XBANK -0.000130[0.0000978]

Ramadan XBANK 0.000672∗∗∗

[0.000159]

DWJONES XBANK 0.0418[0.0519]

DD XBANK -0.000226[0.000158]

DDD XBANK 0.00272∗∗∗

[0.000350]

XHOLD 0.000147[0.0000973]

Ramadan XHOLD 0.000112[0.000166]

DWJONES XHOLD 0.0164[0.0519]

DD XHOLD -0.0000497[0.000160]

DDD XHOLD 0.000143[0.000388]

XTCRT 0.0000744[0.0000980]

Ramadan XTCRT -0.000834∗∗∗

[0.000157]

DWJONES XTCRT -0.0568[0.0500]

DD XTCRT 0.000178[0.000160]

DDD XTCRT -0.000733∗

[0.000383]

Constant -0.000438∗∗∗ -0.000433∗∗∗ -0.000450∗∗∗ -0.000445∗∗∗

[0.0000967] [0.0000969] [0.0000969] [0.0000974]



16


(1) (2) (3) (4)


[0.00807] [0.00799] [0.00801] [0.00800]


[0.0519] [0.0475] [0.0500] [0.0483]

XTRZM 0.000299∗∗∗

[0.0000951]

Coup -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00280] [0.00278] [0.00278] [0.00279]

Terror w/ inst. (T ) -0.000140 -0.000123 -0.000199 -0.000126[0.000156] [0.000156] [0.000145] [0.000157]

Ramadan (Ram) 0.00372∗∗∗ 0.00370∗∗∗ 0.00377∗∗∗ 0.00378∗∗∗

[0.000175] [0.000171] [0.000173] [0.000166]

Treat Ramadan -0.00377∗∗∗ -0.00378∗∗∗ -0.00399∗∗∗ -0.00386∗∗∗

[0.000392] [0.000385] [0.000350] [0.000386]

Ramadan XTRZM 0.000187[0.000166]

DWJONES XTRZM -0.180∗∗∗

[0.0520]

DD XTRZM 0.000233[0.000160]

DDD XTRZM -0.00110∗∗∗

[0.000392]

XUSIN 0.000108[0.0000965]

Ramadan XUSIN 0.000584∗∗∗

[0.000160]

DWJONES XUSIN 0.266∗∗∗

[0.0478]

DD XUSIN -0.0000907[0.000159]

DDD XUSIN -0.000777∗∗

[0.000379]

XSGRT -0.000675∗∗∗

[0.0000888]

Ramadan XSGRT -0.000515∗∗∗

[0.000164]

DWJONES XSGRT -0.0160[0.0501]

DD XSGRT 0.00110∗∗∗

[0.000144]

DDD XSGRT 0.00249∗∗∗

[0.000352]

XUHIZ -0.000272∗∗∗

[0.0000971]

Ramadan XUHIZ -0.000900∗∗∗

[0.000159]

DWJONES XUHIZ 0.187∗∗∗

[0.0486]

DD XUHIZ 0.00000842[0.000160]

DDD XUHIZ 0.000633∗

[0.000383]

Constant -0.000469∗∗∗ -0.000452∗∗∗ -0.000399∗∗∗ -0.000429∗∗∗

[0.0000949] [0.0000961] [0.0000860] [0.0000962]



17


(1) (2) (3) (4)


[0.00806] [0.00784] [0.00795] [0.00804]


[0.0478] [0.0501] [0.0492] [0.0449]

XGMYO -0.0000308[0.0000981]

Coup -0.0463∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00279] [0.00278] [0.00278] [0.00278]

Terror w/ inst. (T ) -0.000158 -0.000133 -0.000104 -0.000206[0.000154] [0.000158] [0.000153] [0.000136]

Ramadan (Ram) 0.00369∗∗∗ 0.00376∗∗∗ 0.00376∗∗∗ 0.00377∗∗∗

[0.000164] [0.000170] [0.000172] [0.000175]

Treat Ramadan -0.00374∗∗∗ -0.00382∗∗∗ -0.00377∗∗∗ -0.00396∗∗∗

[0.000374] [0.000392] [0.000386] [0.000378]

Ramadan XGMYO 0.000667∗∗∗

[0.000156]

DWJONES XGMYO 0.224∗∗∗

[0.0481]

DD XGMYO 0.000487∗∗∗

[0.000158]

DDD XGMYO -0.00178∗∗∗

[0.000372]

XGIDA -0.000337∗∗∗

[0.0000953]

Ramadan XGIDA -0.000402∗∗

[0.000164]

DWJONES XGIDA -0.117∗∗

[0.0501]

DD XGIDA -0.0000194[0.000160]

DDD XGIDA -0.00000882[0.000391]

XILTM -0.0000965[0.0000979]

Ramadan XILTM -0.000421∗∗

[0.000166]

DWJONES XILTM 0.0948∗

[0.0495]

DD XILTM -0.000439∗∗∗

[0.000156]

DDD XILTM -0.00132∗∗∗

[0.000383]

XKAGT -0.000599∗∗∗

[0.000100]

Ramadan XKAGT -0.000641∗∗∗

[0.000166]

DWJONES XKAGT -0.246∗∗∗

[0.0450]

DD XKAGT 0.00128∗∗∗

[0.000141]

DDD XKAGT 0.000913∗∗

[0.000375]

Constant -0.000441∗∗∗ -0.000417∗∗∗ -0.000435∗∗∗ -0.000417∗∗∗

[0.0000969] [0.0000947] [0.0000972] [0.0000973]



18


(1) (2) (3) (4)


[0.00801] [0.00801] [0.00812] [0.00799]


[0.0535] [0.0469] [0.0474] [0.0496]

XKMYA 0.000970∗∗∗

[0.0000671]

Coup -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗ -0.0462∗∗∗

[0.00278] [0.00279] [0.00277] [0.00279]

Terror w/ inst. (T ) -0.0000771 -0.000132 -0.0000377 -0.000133[0.000141] [0.000156] [0.000129] [0.000157]

Ramadan (Ram) 0.00365∗∗∗ 0.00371∗∗∗ 0.00379∗∗∗ 0.00374∗∗∗

[0.000146] [0.000173] [0.000169] [0.000174]

Treat Ramadan -0.00362∗∗∗ -0.00391∗∗∗ -0.00376∗∗∗ -0.00386∗∗∗

[0.000328] [0.000376] [0.000374] [0.000388]

Ramadan XKMYA 0.00125∗∗∗

[0.000138]

DWJONES XKMYA -0.118∗∗

[0.0536]

DD XKMYA -0.000771∗∗∗

[0.000148]

DDD XKMYA -0.00301∗∗∗

[0.000321]

XUMAL -0.000188∗

[0.0000971]

Ramadan XUMAL 0.000385∗∗

[0.000163]

DWJONES XUMAL 0.266∗∗∗

[0.0471]

DD XUMAL 0.00000341[0.000159]

DDD XUMAL 0.00181∗∗∗

[0.000374]

XULAS -0.0000157[0.0000971]

Ramadan XULAS -0.000932∗∗∗

[0.000157]

DWJONES XULAS 0.209∗∗∗

[0.0476]

DD XULAS -0.00151∗∗∗

[0.000128]

DDD XULAS -0.000847∗∗

[0.000374]

XELKT 0.000240∗∗

[0.0000957]

Ramadan XELKT -0.0000837[0.000165]

DWJONES XELKT 0.0829∗

[0.0498]

DD XELKT 0.0000718[0.000160]

DDD XELKT 0.000274[0.000384]

Constant -0.000509∗∗∗ -0.000433∗∗∗ -0.000442∗∗∗ -0.000457∗∗∗

[0.0000674] [0.0000963] [0.0000967] [0.0000954]



19

B Detailed results

The results from the fixed-effect regression indicates that terrorism has a generally negative effect on

stock returns in the 16 indices, when controlling for lagged daily returns, the returns of the U.S. Dow

Jones, the coup attempt, and Ramadan. When instrumented with rainfall, the negative effect from

terrorism increases from 0.03776 to 0.04288, as well as decreasing the p-value of the Terror coefficient

from 0.037 to 0.02. Thus, the instrumentation of the rainfall reflects a larger and more consistent

effect of terrorism on the indices. Next, we interact Tt with Ramadan to see the effects of terrorism

on returns during Ramadan. We find that terrorism negatively affects the returns of the stock market

during Ramadan. The instrumentation of the Terror variable slightly decreases the negative effect of

terrorism from .38858 to .38308 and the p-value marginally increased. Overall, instrumenting rainfall

to terrorism increases the effects of terrorism to the Turkish stock market and statistical significance of

the effect, while slightly lowering the effects of terrorism on the stock market during Ramadan.

The (Tt×Ii) variable captures the effect of terrorism on Index i. Specifically, it compares the returns

of each index before and after the first attack. Of the 16 indices, five of them showed statistically

significant effects from terrorism. Terrorism negatively affects the transportation, telecommunications,

and petroleum industries; while positively affects the paper and real estate industries. The returns of the

transportation index experiences the greatest negative impact from terrorism with a (Tt× Ii) coefficient

value of -0.151, while the wood, paper, and printing index is the least affected index by terrorism with

a coefficient value of 0.128. However, the scope of this model is limited, as the difference-in-difference

(DD) estimator is only able to identify five indices affected by terrorism. By introducing an additional

parameter into the difference-in-difference methodology, the model provides substantially more insight

into how terrorism affects different segments of the economy in Turkey.

While the difference-in-difference (DD) model provides some insight into how terrorism affects these

indices, the results are very limiting because they only found five affected indices. By implementing Ra-

madan as an interaction variable in the difference-in-difference (DDD) methodology, the model captures

12 affected indices affected by terrorism, relative to the five indices captured by the original difference-in-

difference model. Even with the addition of the Ramadan variable, the statistically significant (Tt × Ii)

coefficients from the original regressions remained relatively unchanged. None of the five industries that

shows effects in the DD model change signs or became statistically insignificant when we implemented

Ramadan as an additional interaction variable. We also see a slight increase in the overall r-square value

in the DDD regressions, which should be expected as we add more regressors to the model. Therefore,

with the implementation of Ramadan as an additional interaction variable, we can see effects of terrorism

in seven additional indices without altering the results yielded in the DD model.

Of the 16 indices analyzed, 12 of them show statistically significant effects from terrorism during

Ramadan. Specifically, the (Ram × Tt × Indexi) variable captures the difference between the daily

returns during Ramadan in the control period to the daily returns in the Terror period for index i. The

results show that terrorism negatively affects the transportation, petroleum, wholesale retail trade,

tourism, industrial, real estate, and telecommunication industries during Ramadan; while positively

affects the paper, banking, financial, insurance, and services industries. Terrorism negatively affects the

20

returns of chemical, petroleum, and plastic index the greatest during Ramadan with a (Ii×Rami× Tt)coefficient value of -0.301, while the banking industry has the greatest positive effect with a coefficient

value of 0.272.

Overall, instrumenting rainfall to terrorism increases the effects of terrorism to the Turkish stock

market and statistical significance of the effect, while slightly lowering the effects of terrorism on the

stock market during Ramadan. The results from the fixed-effects regression indicates that terrorism

has a generally negative effect on stock returns in the 16 indices, when controlling for pertinent factors

and events. When instrumented with rainfall, the negative effect from terrorism increases from 0.03776

to 0.04288, as well as decreasing the p-value of the Terror coefficient from 0.037 to 0.02. Thus, the

instrumentation of the rainfall reflects a larger and more consistent effect of terrorism in the fixed-effects

regression.

Next, we interact Tt to Ramadan to see the effects of terrorism on returns during Ramadan. We find

that terrorism negatively affects the returns of the stock market during Ramadan. The instrumentation

of the Terror variable slightly decreases the negative effect of terrorism from 0.38858 to 0.38308 and

the p-value marginally increased. However, there is not a significant difference between the original

and instrumented coefficients, thus rainfall does not further explain the relationship between terrorism

during Ramadan and stock returns.

21

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Terrorism, stock market returns, and volatility: A di … › 2016 › 02 › ...Terrorism, stock market returns, and volatility: A di erence-in-di erence approach Eric Lenz and Brian