macroeconomic variables and stock market volatility: the case … · 2017-10-03 · examined the...

Macroeconomic Variables and Stock Market Volatility: the Case

of Turkey, Israel and Cyprus

Yousef Daoud1

Fairouz Darwish2

Muniece Zurub El Far3

1 Associate Professor, Economics Department, College of Business and Economics, Birzeit University.

2 Instructor, Finance Department, College of Business and Eonomics, Birzeit University.

[email protected] . Tel 00972-02-2982185 3 Instructor, Finance Department, College of Business and Economics, Birzeit University.

Abstract

This paper analyzed stock market returns and their interplay with six macroeconomic indicators-

gross domestic product , interest rate , balance of trade , consumer price index , exchange rate

and stock market index- for the period 2005 - 2012 using monthly data for three countries,

Cyprus, Israel and Turkey. We used time series models to investigate which of the

macroeconomic variables affects stock market volatility, as well as a long run and short run

causality. The evidence presented shows that each country was different, but the similarities

between Israel and Turkey were more than with Cyprus. We found that the stock market returns

do not follow a normal distribution for any of the three countries. We also found that the 2008

crisis worsened volatility for all three countries. The Vector Error Correction Model (VECM

)analysis pointed to a long run causality between macroeconomic variables and the stock market

return, particularly for Israel and to a lesser degree for Turkey.

Keywords: Stock Market, Volatility, EGARCH, VECM

JEL Classification

E44-G17-C5-C1

Introduction

Volatility of stock market index (hence returns) has been a central theme in the study of financial

markets because it could end up in a collapse of the financial system as what happened in the late

1920’s and 1930’s. The more recent 2008 crisis did not end in contagion because of the lessons

learned from the past. Volatility might be spurred by expectations and news; leading to stock

market adjustments. The consequences of stock market collapse can be so severe that it sends the

economy in a downward spiral effecting growth, unemployment, and poverty. Exploring and

defining factors that affect volatility can lead to better management of currency risk and better

hedging strategies. Understanding the relation between market and exchange rate can enhance

monetary and fiscal policies, as well as it can set the basis for informed choices regarding

investment decisions. Ewing (2012), among others, investigated the interaction between

macroeconomic variables and stock market return. It was argued that stock prices were often

determined by some economic variables such as interest rate, inflation, GDP and the exchange

rate. He argued that macroeconomic variables had a crucial impact on stock markets’ participants

risk management strategies.

The study of stock market volatility has been more prevalent for western and industrial

economies, but less so for the Near East South Asia (NESA) countries4. This study, therefore,

examines the nexus between the stock market return volatility and macroeconomic variables in

Turkey, Cyprus and Israel for a period of 8 years, from 2005 to 2012. Theoretical evidence of the

relationship between the three stock markets return and each country’s macroeconomic variables

was investigated using Exponential Auto-Regressive Conditional Heteroskedasticity ( E-

GARCH) and Vector Error Correction (VEC) models. Our study allows us to test whether

volatility clustering is evident for the three countries or not; meanwhile, the VECM model would

help in analyzing long run and short run dynamics, as well as weak exogeneity tests. The paper

will test for long run granger causality (error correction coefficient) to identify whether stock

market is mean reverting as postulated by theory or not.

4 Our data set also includes Cyprus which is not a NESA country, but its closeness to the region and membership in

the European Union encouraged us to include it in the study.

This paper is organized as follows: Section 2 reviews previous literature on the relationships

between macroeconomic variables and stock market return, Section 3 describes the data and the

econometric methods used in the research variables, Section 4 shows the results and analysis,

and Section 5 concludes.

Literature review

The literature on stock market volatility is abundant. The 2008 financial crisis has spurred the

renewed interest in volatility and its measurement. Agrawal et.al, (2010) pointed out that arrival

of new information regarding dividends, money supply, employment, gross domestic product,

interest rate and currency exchange rate are considered the main sources of volatility. There are

many factors which affect short-term and long-term volatility.

The advent of floating exchange rate regime in the early 1970’s, Asian currency crises and the

early 1990’s financial market reforms prompted financial economists into determining the link

between stock markets and currency exchange rate markets (Adjasi et.al, 2002). However,

Schwert (1989) admitted that there was no theoretical consensus on the interaction between

currency exchange rates and stock prices.

Because of today’s globalization and the increase of international fund mobility among the

world’s capital markets, foreign exchange rate has become the most important factor that affects

any stock market. According to Anlas (2012), exchange rate can be viewed as a national and

international indicator that responds quickly to political and social changes. The exchange rate

can have a direct impact on input and output prices and therefore influences any firm’s

international competitiveness, sales, profitability and in turn their stock prices. When the

exchange rate appreciates, the exporters’ profits, sales and stock prices will decline. However,

the importers’ profits and stock prices will increase. Conversely, if the stock market experiences

a downward movement, then demand for funds will decrease causing the interest rate to

deteriorate which results in currency depreciation (Agrawal et. al, 2010).

According to Yang and Doong (2004), the change in exchange rates which is caused by

imbalances between demand and supply of funds is explained through the flow-oriented

approach. These imbalances affect the international trade and competitiveness of a country

which, on the other hand, influence the country’s real income and the output produced. Whereas,

the stock-oriented approach models highlight the link between the stock market and currency

exchange rate through equating demand and supply for capital assets. Thus, currency exchange

movements may influence or are influenced by stock prices. As the currency depreciates,

investors will be motivated to move funds from domestic markets which results in depressing

stock prices ((Adjasi et.al,2002).

The empirical literature shows mixed evidence on the relationship between stock market and

foreign exchange market volatility. Agrawal et. al (2010) investigated the Indian Rupee-US

Dollar exchange rate and the Indian stock market return. The relationship was proved to be

negative and unidirectional running from stock market return towards exchange rate. Moreover,

Sekmen (2011) examined the effect of exchange rate volatility on the stock returns of the US

stock market. The result was a negative relationship; higher exchange rate volatility generated

low stock returns although US companies had used hedging strategies to minimize the currency

exchange risk. Jiranyakal (2012) explored an emerging market namely in Thailand and the

results showed that there was no long-run relationship between the stock market index and the

exchange rate. However, unidirectional causality between stock market index and exchange rate

exists. Fu et. al ‘s (2011) findings indicate that news shocks associated with foreign exchange

market generate volatility transmission to eight out of ten Japanese industrial sectors

corporations’ stock prices. Kumar (2013) indicated the existence of bi-directional volatility

between foreign exchange and stock markets in India, Brazil and South Africa.

On the other hand, the stock market shapes any country’s development. GDP, CPI and short-

term interest rate should be measured when analyzing the stock market activity. Wang (2011)

tested the relationship between the volatility of China’s stock market and the real GDP, inflation

and interest rate. His findings indicated that China’s stock market did not reflect changes of

macroeconomic variables except for inflation. In their study, Oseni and Nwosa (2011) also

examined the relationship between volatility in Nigerian stock market and macroeconomic

variables, and the result of the analysis was a bi-causal relationship between the real GDP and

stock market volatility. the study implied that inflation and interest rate were not significant in

explaining Nigerian stock prices and vice versa. Mushtaq et.al (2012) investigated the

relationship between the Pakistan stock market volatility and macroeconomic variables. The

findings revealed the strong significant relationship between CPI and the foreign direct

investment and stock market volatility, while they showed a negative association between T-bill

rate, exchange rate and the Pakistan’s stock market volatility.

Data and Descriptive Statistics

Monthly data5 on three countries were collected for the following variables: stock market index,

GDP, CPI, trade balance (BOT), exchange rate6, and the interest rate. The data span is from

January 2005 to December 2012, making a total of 96 observations on each of the three countries

(Cyprus, Turkey, and Israel).

The ADF test was applied to test the data for stationarity using three varieties of the ADF

regression. The non-stationary null was not rejected for any of the variables except in two

instances, the CPI for Turkey and the trade balance for Cyprus (one of the three variants in each

case). However, the variables were found stationary in the first difference. To account for

structural breaks in the data, Zivot and Andrews (1992) pointed out that unit root tests may yield

biased results in the presence of structural breaks. The results of Zivot-Andrews unit root test

(Table 2) show that all variables are non-stationary except for the stock market data for all the

three countries; this suggests that the non-stationarity found by the ADF was plagued by possible

structural breaks in the data.

A normality test was applied to all the variables for Cyprus, Israel and Turkey to determine the

nature of their distributions. For this purpose, Jarque-Bera statistics were computed, which are

shown in Table 2 along with descriptive statistics for all the other variables. Skewness quantifies

how symmetrical the distribution is; a value of 0 for skewness and 3 for kurtosis would indicate

that the variables are normally distributed. If the skewness is greater than 1.0 (or less than -1.0),

then skewness is substantial and the distribution is far from symmetrical. In addition, high or low

kurtosis value indicates extreme leptokurtic (peak) or extreme platykurtic (flat). From the

obtained statistics, it is evident that the skewness of all variables is in the range of 1 to -1, and the

5 The GDP data were quarterly and converted to monthly using STATA's Denton method.

6 The exchange rate series for the selected currencies were floating during the study span. The Cypriot

Pound was pegged to the US $ at .5269539 CYP until Cyprus adopted the EURO on January 2008.

kurtosis is less than 3 except for BOT for Cyprus. As a result, the variables are non-normally

distributed, as the skewness values for all variables are in the range of-0.868754 and 0.798018

and the kurtosis values are 2.70545 and 1.3996 respectively.

Figure 1: Stock market return of the three economies

-.6

-.4

-.2

.0

.2

.4

2005 2006 2007 2008 2009 2010 2011 2012

Cyprus Israel Turkey

Figure 1 reveals that volatility has increased after 2008, particularly for Cyprus. It is also evident

that volatility clustering is not uniform for all the three economies. While the return for Israel has

the lowest variance, Cyprus has the highest. The 2008 crisis seems to have affected the Turkish

markets the most and the Israeli market the least, although the Turkish return rebounded faster

than the other two markets.

The model

Evidence and concern over stock market volatility has attracted the attention of practitioners and

policy makers alike; consequently, calls have been suggested to limit volatility through various

measures. Stock prices change more rapidly in response to new information, high level of

transactions and liquidity of securities markets (Schwert, 1990). Our data spans a period of pre

and post 2008 crisis allowing us to investigate the magnitude of volatility for the two sub-

periods. The most common measure of volatility is the standard deviation which captures the

dispersion of returns. In a model where the dependent variable is stock market return, it is

assumed that the error variance is constant; however, Engle (1982) noticed that this assumption

is often violated using time series data. The Autoregressive Conditional Heteroskedastic model

(ARCH) suggested by Engle (1982) was widely used to capture persistence in stock market

volatility. Later, Bollerslev (1986) generalized the ARCH into GARCH where the error variance

follows GARCH(p,q) allowing for better modeling. The ARCH and GARCH models do capture

volatility clustering, however, they have symmetric distributions. The Exponential GARCH

(EGARCH) model, developed by Nelson (1991), allows for non-linearities in their specification7.

Volatility clustering is often found in high frequency time series models; it implies that periods

of high volatility are likely to be followed by high volatility. Since the variance is an indicator of

risk, then finding EGARCH effects is tantamount to the existence of time varying risk.

The EGARCH model assumes that the mean equation (stock market return, in our case Y) for a

country is a stationary process whose mean value depends on information set I and a stochastic

error ɛ:

tttt IYEY )|( 1 (1)

The random innovations ɛt are assumed to have a standard normal distribution with a certain set

of parameters for its probability distribution (ɛt~N(0, ht).

ttt vh 5.0 (2)

with vt ~ N(0,1)

q

j

jtjkt

r

k

k

p

i

itit hvvh111

log||log

(3)

Equation (3) assumes that ɛt follows a generalized error distribution (Nelson 1991)8. The left

hand side of equation (3) is the conditional variance of innovations in the mean equation;

predictions are thus guaranteed to be non-negative as expected even if the parameters are

7 Other non-linear specifications are GJR-model of Glosten et al (1993) and the APARCH of Ding et al (1993).

8 Eviews offers the choices of normal, t, and GED specifications.

negative. The parameter λ signifies the GARCH effect (or often called the symmetric effect),

while θ represents the asymmetric effect (or leverage effect). This means that one is able to test

hypothesis about the existence of asymmetries by testing whether θ is zero or not; if it turns out

that it is zero, then there are no asymmetries. This implies volatility clustering is symmetric (i.e

innovations effect is similar in different time periods). But if θ is positive, then this implies that

positive shocks tend to increase volatility and negative shocks tend to decrease volatility.

However, when θ is negative, then positive shocks lower volatility and negative shocks will do

the opposite. Thus, one can conclude that good news are more destabilizing than bad news when

θ > 0. The parameter β signifies persistence in the conditional variance, which may be short or

long lived depending on the value of q.

Empirical results

We begin our analysis by investigating the normality of stock returns for the three countries; The

Jarque-Bera test of normality rejects the null at the 1% level or less (Table 4), which is in line

with the empirical literature (Officer 1972 and Blume 1968). More recently, Kumar and Dhankar

(2011) found that the Indian stock market returns of daily and weekly frequency were non-

normal, while monthly and annual data were normally distributed. Although we have utilized

monthly data, we have found that the distributions of stock returns were left skewed for Turkey

and Israel but right skewed for Cyprus. The Kurtosis figures imply a leptokurtic distribution

which is an obvious departure from the normal distribution. Hence, we estimate three variants of

model (1); the first assumes that the constant is the only regressor, the second adds an

autoregressive component of order one for the errors, and the third includes the constant and a

set of macroeconomic variables (included in Table 2). For each case, we perform the Jarque-Bera

test of normality on the residuals from that equation. Table 3 below summarizes the residuals'

diagnostics. The Akaike Information Criterion (AIC) renders model 1 as best for Cyprus and

Turkey and Model 2 for Israel.

We estimate different variants of equations (1) and (3) assuming that innovations follow

different distributional assumptions and maintaining that the mean equation also has various

specifications. The best model from those many specifications was chosen based on AIC and log

likelihood. The results are reported in Table 5. The error distribution for Turkey and Israel was

indicated to be Generalized Error Distribution (GED) and for Cyprus to be t-distribution. The

results indicate variations at the country level; while ARCH effects are significant only for

Turkey and Israel, the leverage effect is significant for Cyprus only. The GARCH effect is not

significant for Cyprus implying that changes in innovations have asymmetric impact on the

Cypriot stock market return volatility. Since the leverage effect is positive, it implies that

positive shocks increase volatility (good news are more destabilizing than bad news). But it does

not seem that any of the macroeconomic variables seem to affect volatility. This is not the case

for Turkey, The GARCH effect is negative but symmetric, asymmetries are non-existent, and

there is evidence of conditional variance persistence. This also suggests stock market volatility.

The trade balance and GDP have similar positive impact on volatility (although the trade balance

effect is nearly non-existent); however, inflation tends to lower volatility. The situation for

Israeli data has features of both; like Cyprus, none of the macroeconomic variables are

significant in the variance equation, meanwhile, ARCH and GARCH are significant but the

leverage effect is not. Finally, the 2008 crisis has increased volatility of the stock market returns

for all the three countries.

The Vector Error Correction (VEC) model of Johansen (1988) captures long run as well as short

run dynamics of a system of equations. The use of VEC requires that the series be I(1) and are

co-integrated; Johansen (1995) showed that there could be five cases that capture the

deterministic trend (depending on whether they are restricted or not). To test for the number of

cointegrating vectors, we apply the trace and maximum Eigen value tests to each of the five

cases using different lags. The test results on the number of cointegrating vectors vary between

one and five depending on the case and choice of lag. The highest log likelihood indicates 4 lags;

however, irrespective of the number of lags, the trace statistic and the maximum Eigen value

statistic are most frequently in agreement on two cointegrating relations.

Suppose that the VAR(p) model is

tPtPttt YYDY 11 (3)

where Yt is a 6x1 vector of I(1) variables, and Dt captures deterministic terms; if Yt is

cointegrated, then equation (3) can be written as a VEC model

tPtPtttt YYYDY 111111 (4)

where

np I 1 (5)

and

1,,1,1

pk

p

kj jk (6)

The term Π Yt-1 is of primary interest as it contains the cointegrating relations when they exist

such that the error correction term is stationary. When this term is stationary, the matrix Π has a

reduced rank (0 < r < 6 in this case) independent cointegrating relations. In such a case, we can

decompose the Π matrix: ' = 2X66x266 x assuming two cointegrating relations as

discussed above. The vector α represents reactions of the vector Yt to deviations from long run

equilibrium relationships, and β is the cointegrating vector. The model selection was based on

the summary provided in Table (6). For Cyprus and Turkey, there will be 2 cointegrating

relations and 4 lags; for Israel, there will be 2 cointegrating relations and 3 lags. Table 6 implies

deterministic trends case d (linear trend in the data, intercept and trend in cointegrating relation)

for Israel, e (quadratic trend in the data, intercept and trend in cointegrating relation) for Cyprus,

and c (linear trend in the data, intercept and no trend in cointegrating relation) for Turkey

(default as the three criteria are not in agreement).

Estimates of the VECM for the three countries are reported in Tables (7-9). The first two rows of

each Table represent the adjustment coefficients of the endogenous variable in question to

deviations from the long run equilibrium relationship. Since there are two cointegrating

relationships, then the second row shows the coefficients of the second relationship. It is

expected that the coefficients have a negative sign to indicate if a variable deviates from a long

run relationship; short run dynamics ensure it converges to its long run equilibrium relation.

Table 7 reports the results for Cyprus; it is evident that the interest rate only has the correct sign

and simultaneously significant. This implies that if the interest goes above its long run

equilibrium value, it is eventually brought back to its equilibrium level by the other significant

variables in the system. Most notably, an increase in stock market returns with 1 and 2 lags

lowers the change in the interest rate. Other variables that are significant are own lags, price

level, and the GDP. The 2008 dummy is significant and negative for the stock market return and

the interest rate. However, it is positive and significant for the exchange rate.

For Turkey, it is found that the stock market return and the interest rate both have the correct

sign and significant . The GDP tends to significantly increase the stock market return, while the

trade balance worsens it. As for the interest rate, own lags are significant while the other

variables are not. The 2008 crisis seems to have negatively (and significantly) affected the stock

market and GDP; the impact on the trade balance and the exchange rate was positive. Finally,

Israel's results are reported in Table 9; the error correction term is significant for the stock return,

the interest rate, the trade balance, and the exchange rate. The only short run adjustment of the

Israeli stock market return comes through the exchange rate. The interest rate responds positively

to own lags and similarly to the trade balance. Similar to the previous two countries, the 2008

crisis has had a negative impact on the stock market and the interest rate.

It is often of interest to test restrictions on the vectors β and α. Since the matrix α6x2 contains

error correction coefficients, one can test for weak exogeneity of the ith

variable by restricting the

coefficients of the ith

row to zero in the α matrix. This implies that the ith

variable (the weakly

exogenous variable) does not adjust to deviations from the long run relationships while the other

variables in the system do9

. For example if we want to test for weak exogeneity of the exchange

rate to the other variables in the system, we impose zero values for a(6,1) and a(6,2). If the null is

rejected, then the variable of interest is not weakly exogenous, which implies long run causality;

in other words its values are not taken as given.

The weak exogeneity test is implemented with the specifications given in tables 7-9. Table 10

provides the likelihood ratio test of weak exogeneity; it is found that the interest rate and the

trade balance are highly significant for Cyprus, which means weak exogeneity is rejected only

for these variables; for the other variables in the system, weak exogeneity is not rejected

implying that they do not adjust to deviations from the long run relations, but the interest rate and

trade balance do. For Turkey, weak exogeneity is not rejected for the interest rate and the CPI;

9 Assuming none of them is weakly exogenous also.

this implies that these variables do not adjust to deviations from the long run equilibrium. This

means that the other variables (including the stock market return) react to deviations from

equilibrium. Finally, for Israel, only the CPI is found to be weakly exogenous which means the

rest of the variables are bound by the long run causality.

In summary, the evidence for Israel and Turkey points in the direction of long run causality

between macroeconomic variables and the stock market return; the consumer price index is

uniformly not rejected for all the three countries indicating that its values can be taken as

determined outside the system. But the trade balance is uniformly rejected implying that it is

strongly affected by the other variables in the system.

Conclusions

This paper deals with the stock market index as the primary variable of interest; it analyzed the

stock market index and its relationship with macroeconomic indicators. In the first case, the

EGARCH model was applied to the data set in order to investigate volatility clustering and

asymmetry.

Tests of normality of the stock market returns for the three countries imply non-normality; thus

implying the inapplicability of standard inferential procedures. The EGARCH model residuals

were also found to be either GED or t - distributed. The ARCH effect was significant for Turkey

and Israel which implies persistence in the conditional variance of the stock market return; in

other words, shocks to the conditional variance affect the future values and do not die out the

following period. The GARCH effect seems to be asymmetric for Cyprus but symmetric for

Turkey and Israel. There are differences among the countries in how macroeconomic variables

affect the volatility of returns. For Israel and Cyprus, none of the macroeconomic variables is

significant in explaining volatility; for Turkey, positive news about GDP and trade balance

increase volatility while inflation lowers it. The impact of the 2008 crisis on volatility was found

to be positive; volatility was higher on average in the following period than in the prior period

covered in the study.

The error correction model analysis established that there are at least two cointegrating

relationships among the variables in the model. This implies that in the long run, the stock

market return is influenced by the macroeconomic indicators with varying degrees. For example,

focusing on the stock market equation, the adjustment coefficient is negative and significant for

Turkey and Israel. In this case, deviations from long relationship do not have impact on the

stock market return for Cyprus, but they do for Israel and Turkey. On the other hand, the interest

rate is negative and significant for all the three countries. The evidence presented in the paper

points out to a long run causality between macroeconomic variables and stock market return, but

the case is not so for Cyprus.

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Statistical Annex

Table A1: Zivot-Andrews Unit Root Test

Intercept

Intercept

and

Trend Trend Only

Balance of trade -4.2169 -4.54 -2.94

Cons. price index -4.9162 -5.47 -4.53

Exchange rate -4.1377 -4.50 -3.99

GDP -4.2348 -5.25 -2.27

Interest rate -10.30* -10.49* -10.2958*

Stock Market Index -3.5076 -3.53 -2.63

Balance of trade -4.76 -4.76 -3.36

Cons. price index -6.48 -6.23 -5.31

Exchange rate -3.61 -4.01 -3.30

GDP -4.18 -4.78 -2.82

Interest rate -13.84* -13.72* -13.30*


Balance of trade -4.44 -5.63* -4.47

Cons. price index 3.88 -3.85 -2.04

Exchange rate -7.63 -8.69* -2.70

GDP -2.65 -3.20 -2.72

Interest rate -12.24* -13.96* -11.08*


Criticle value 5% -4.93 -5.08 -4.42

Variable

Israel

Turkey

Cyprus

*Significant at the 5% level

Table 2: Sample descriptive (data)

Balance of

trade

Cons.

price

index

Exchange

rateGDP

Interest

rate

Stock

Market

Index

Mean -5526372.0 160.1 1.5 8605.0 0.1 1863.6

Median -5299392.0 160.7 1.5 8528.1 0.1 1888.1

Skewness -0.315 0.111 0.248 0.298 -0.082 -0.159

Kurtosis 2.693 1.863 2.216 2.102 1.203 2.435

Jarque-Bera 1.967 5.363 3.440 4.640 13.017 1.680

Probability 0.374 0.068 0.179 0.098 0.001 0.432

Observations 96 96 96 96 96 96

Mean -868.8 91.2 4.0 68730.4 0.0 941.9

Median -813.3 91.0 3.9 68653.3 0.0 981.6

Skewness -0.372 0.123 0.340 -0.042 -0.143 -0.447

Kurtosis 2.705 1.575 2.167 2.036 2.202 2.268

Jarque-Bera 2.566 8.359 4.622 3.746 2.878 5.335

Probability 0.277 0.015 0.099 0.154 0.237 0.069


Mean -406433.3 109.5 0.6 1369.3 0.0 1732.6

Median -396460.0 110.2 0.7 1412.6 0.0 1289.1

Skewness -0.869 -0.001 -0.354 -0.728 0.409 0.798

Kurtosis 4.370 1.890 1.400 2.322 2.295 2.511

Jarque-Bera 19.583 4.932 12.252 10.325 4.660 11.144

Probability 0.000 0.085 0.002 0.006 0.097 0.004


Israel

Turkey

Cyprus

Table 3: Diagnostic statistics on mean equation residuals

model 1 model 2 model 3

Akaike 0.705 0.724 0.711

Skewness 6.880 6.890 6.690

Kutosis 60.770 60.660 57.860

Jarque-Bera 13958 13764 12623

p-value 0.000 0.000 0.000

Akaike -1.396 -1.370 -1.313

Skewness -1.013 -0.958 -1.027

Kutosis 5.420 5.252 5.170

Jarque-Bera 39 34 35

p-value 0.000 0.000 0.000

Akaike -2.932 -2.998 -2.880

Skewness -0.718 -0.476 -0.663

Kutosis 3.749 3.255 3.674

Jarque-Bera 10 4 9

p-value 0.006 0.149 0.013

Cyprus

Turkey

Israel

Table 4: Stock market returns' diagnostics

Cyprus Turkey Israel

mean 0.0033 0.008 0.0056

Median -0.01 0.035 0.015

Skewness 6.88 -1.013 -0.718

Kurtosis 60.77 5.42 3.75

J-B 13958.8 39.45 10.38

P-value 0 0 0.006

Table 5: Maximum likelihood estimates of the EGARCH (1,1) model

Coeff P-value Coeff P-value Coeff P-value

Constant_M 0.015 0.114 0.013 0.039 0.004 0.268

Constant_V -82.190 0.076 -4.135 0.000 -5.061 0.812

garch -0.669 0.161 -0.981 0.000 -0.897 0.000

leverage 0.745 0.041 -0.175 0.121 -0.104 0.454

arch -0.027 0.951 0.584 0.000 0.597 0.000

log price level 16.725 0.264 -0.330 0.000 0.192 0.955

log exchange rate 3.808 0.399 -0.086 0.885 1.116 0.238

log gdp 0.487 0.963 0.539 0.000 0.084 0.947

interest rate 4.695 0.912 -0.116 0.970 -3.220 0.759

trade balance 0.000 0.731 0.000 0.040 0.000 0.504

zivot structural break -1.139 0.613 0.070 0.501 0.327 0.324

2008 dummy 2.097 0.066 0.463 0.001 0.778 0.000

AIC

Log-Likelihhod

Israel

-0.782

50.154

-1.493

83.903

-3.116

161.002

Cyprus Turkey

Table 6: Summary table of the model selection criteria

Trace and Maximum

Eigen Value AIC Schwarz

Country p r D p r D p r D

Cyprus 4 2 e 4 2 e 1 1 a

Turkey 4 2 b 4 3 d 1 1 c

Israel 3 2 d 3 2 d 1 1 d

P is the lag order in the vec model

r is the number of cointegrating relations

D is the deterministic trend case

a No deterministic trend in the data, and no intercept or trend in the cointegrating equation

b No deterministic trend in the data, and an intercept but no trend in the cointegrating equation

c Linear trend in the data, and an intercept but no trend in the cointegrating equation

d Linear trend in the data, and both an intercept and a trend in the cointegrating equation

e Quadratic trend in the data, and both an intercept and a trend in the cointegrating equation

Table 7: Vector Error Correction Estimates (Cyprus)

D(log

(Stock

Index))

t-

statistic

D

(Interest

rate)

t-

statistic

D(log

(CPI))

t-

statistic

D(log

(GDP))

t-

statistic

D(Trade

Balance)

t-

statistic

D (log

(e))

t-

statistic

Adj. Coeff -0.0078 -0.4642 0.0017 10.1717 -0.0002 -0.6569 -0.0005 -0.5772 1943.6 0.7969 -0.0026 -0.8395

Adj. Coeff. 10.0574 0.4121 -2.5096 -10.1650 0.3681 0.7840 0.9307 0.8059 -2453890.0 -0.6912 4.1181 0.9012

D(log(Stock Index)(-1)) 0.0278 0.2247 -0.0009 -0.7467 0.0008 0.3457 -0.0027 -0.4639 15414.0 0.8552 -0.0008 -0.0350

D(log(Stock Index)(-2)) -0.0702 -0.5777 -0.0033 -2.6463 0.0031 1.3441 0.0043 0.7508 -18035.8 -1.0200 -0.0150 -0.6577

D(log(Stock Index)(-3)) 0.0084 0.0668 -0.0029 -2.2675 -0.0007 -0.3070 0.0038 0.6289 10603.5 0.5768 0.0060 0.2524

D(log(Stock Index)(-4)) 0.0376 0.2955 0.0013 1.0381 -0.0005 -0.2220 0.0033 0.5448 -11975.9 -0.6473 -0.0044 -0.1843

D(Interest rate(-1)) -3.9147 -0.2316 1.2499 7.3090 -0.1309 -0.4027 -0.3468 -0.4335 1743955.0 0.7092 -2.7818 -0.8789

D(Interest rate(-2)) -4.6883 -0.3620 1.2648 9.6518 -0.2116 -0.8491 -0.3369 -0.5496 1459219.0 0.7744 -2.4774 -1.0215

D(Interest rate(-3)) -6.3268 -0.4561 1.0286 7.3283 -0.0807 -0.3022 -0.2426 -0.3696 2008109.0 0.9950 -2.7510 -1.0590

D(Interest rate(-4)) 16.0128 1.4488 0.4367 3.9051 -0.0419 -0.1972 -0.4423 -0.8455 1123990.0 0.6990 -0.3836 -0.1854

D(log(CPI)(-1)) -0.7474 -0.1157 -0.0704 -1.0774 0.0635 0.5108 0.8338 2.7280 -1056904.0 -1.1249 0.8579 0.7095

D(log(CPI)(-2)) 4.2239 0.7039 -0.1581 -2.6046 -0.3790 -3.2830 0.2943 1.0365 436080.3 0.4995 0.0799 0.0711

D(log(CPI)(-3)) -2.6507 -0.4496 -0.0248 -0.4149 -0.4326 -3.8141 0.7606 2.7259 769618.0 0.8972 0.9745 0.8827

D(log(CPI)(-4)) 11.3501 1.7690 -0.0990 -1.5249 -0.3945 -3.1964 0.0792 0.2607 701758.8 0.7518 0.4994 0.4157

D(log(GDP)(-1)) -2.1826 -0.8316 0.0458 1.7247 0.0543 1.0757 -0.6737 -5.4243 447686.8 1.1725 0.1551 0.3156

D(log(GDP)(-2)) -2.2041 -0.7431 0.0243 0.8088 0.0427 0.7482 -0.7216 -5.1408 -286024.5 -0.6629 -0.3988 -0.7181

D(log(GDP)(-3)) -3.2977 -1.0510 0.0180 0.5663 0.0424 0.7030 -0.4003 -2.6961 -451986.8 -0.9902 -0.2028 -0.3452

D(log(GDP)(-4)) -0.4862 -0.1977 0.0190 0.7616 0.0302 0.6377 -0.0866 -0.7440 -436718.5 -1.2204 -0.6348 -1.3783

D(Trade Balance(-1)) -1.6E-06 -1.1067 5.2E-09 0.3516 2.2E-08 0.7815 2.2E-07 3.1961 -0.3 -1.3610 2.9E-07 1.0506


D(Trade Balance(-3)) -1.2E-06 -0.9417 6.7E-09 0.5296 -2.3E-08 -0.9585 7.0E-08 1.1992 0.1 0.5266 3.5E-08 0.1510

D(Trade Balance(-4)) -9.8E-07 -1.0200 8.0E-09 0.8166 1.7E-08 0.8890 2.7E-08 0.5887 0.2 1.0889 -6.6E-08 -0.3669

D(log(e)(-1)) -0.1378 -0.1960 -0.0001 -0.0204 0.0160 1.1856 0.0688 2.0665 134584.4 1.3159 -0.0263 -0.1995

D(log(e)(-2)) 0.3380 0.4618 -0.0025 -0.3352 0.0053 0.3745 0.0547 1.5789 -68920.6 -0.6473 -0.2595 -1.8941

D(log(e)(-3)) 0.4175 0.5858 0.0019 0.2621 0.0021 0.1567 0.1230 3.6470 80783.7 0.7790 -0.0417 -0.3128

D(log(e)(-4)) 1.2348 1.6053 0.0010 0.1342 0.0085 0.5744 0.0630 1.7313 20076.6 0.1794 -0.0910 -0.6322

Constant 0.2518 2.0917 0.0026 2.1639 0.0027 1.1708 0.0185 3.2502 -8557.2 -0.4886 -0.0005 -0.0212

Trend -0.0042 -2.2081 -2.3E-05 -1.1759 1.3E-05 0.3647 -0.0003 -3.1106 137.3 0.4991 -0.0001 -0.2727

2008 dummy -0.2772 -2.3045 -0.0033 -2.7516 0.0019 0.8385 -0.0074 -1.2982 12162.3 0.6950 0.0598 2.6547 Critical value for t at the 5% level and 63 df is -1.64 and at the 2.5% is 1.96

Table 8: Vector Error Correction Estimates (Turkey)

D(log

(Stock

Index))

t-

statisti

c

D

(Interest

rate)

t-

statistic

D(log

(CPI))

t-

statisti

c

D(log

(GDP))

t-

statisti

c

D(Trade

Balance)

t-

statisti

c

D(log

(e))

t-

statisti

c

Adj. Coeff -0.2541 -4.0782 -0.0207 -2.1000 0.0095 2.0464 0.0085 0.6646 2075357.0 3.6141 0.0702 2.9770

Adj. Coeff. -0.6823 -0.5221 -2.4413 -11.7981 0.0147 0.1510 -0.1828 -0.6839 23295822.0 1.9341 0.0210 0.0424

D(log(Stock Index)(-1)) -0.1200 -0.6218 0.0371 1.2145 -0.0043 -0.2980 -0.0099 -0.2514 853196.6 0.4798 -0.0814 -1.1151

D(log(Stock Index)(-2)) 0.2431 1.2358 0.0514 1.6517 -0.0109 -0.7424 0.0323 0.8025 2218502.0 1.2237 -0.1089 -1.4633

D(log(Stock Index)(-3)) -0.0001 -0.0007 0.0266 0.8697 0.0060 0.4199 0.0287 0.7278 -588172.3 -0.3305 -0.0065 -0.0885

D(log(Stock Index)(-4)) -0.1088 -0.5778 -0.0182 -0.6121 -0.0142 -1.0081 -0.0165 -0.4276 -368881.9 -0.2126 0.0970 1.3608

D(Interest rate(-1)) 0.2105 0.2671 1.2781 10.2464 -0.0134 -0.2280 0.0423 0.2624 -13104488.0 -1.8048 -0.0895 -0.3002

D(Interest rate(-2)) 0.4829 0.6942 1.2322 11.1881 0.0221 0.4267 0.1008 0.7085 -8846071.0 -1.3798 -0.1838 -0.6982

D(Interest rate(-3)) 0.6804 0.9362 1.1198 9.7329 -0.0191 -0.3521 0.0992 0.6677 -11154926.0 -1.6657 -0.0327 -0.1187

D(Interest rate(-4)) 0.3486 0.5337 0.3259 3.1521 0.0190 0.3911 0.0053 0.0399 -8141533.0 -1.3527 -0.1057 -0.4278

D(log(CPI)(-1)) -0.6601 -0.4092 0.0653 0.2557 0.1500 1.2469 0.4084 1.2379 -23388237.0 -1.5732 -0.4377 -0.7170

D(log(CPI)(-2)) -0.1821 -0.1136 -0.0969 -0.3814 -0.2740 -2.2908 0.1650 0.5029 -13502788.0 -0.9134 -0.1352 -0.2227

D(log(CPI)(-3)) -0.0990 -0.0613 -0.3198 -1.2502 -0.1535 -1.2739 -0.4011 -1.2135 8960245.0 0.6017 -0.2270 -0.3711

D(log(CPI)(-4)) -1.4566 -0.8946 0.3319 1.2876 -0.2323 -1.9138 0.0647 0.1942 10730383.0 0.7151 0.1899 0.3082

D(log(GDP)(-1)) -1.4598 -2.0840 0.1025 0.9240 0.1080 2.0679 -0.5503 -3.8409 11601809.0 1.7972 0.5646 2.1297

D(log(GDP)(-2)) -0.7570 -1.1070 0.0582 0.5380 0.1289 2.5276 -0.4050 -2.8956 7743814.0 1.2288 0.3943 1.5237

D(log(GDP)(-3)) -0.6570 -0.9824 0.0704 0.6654 0.1218 2.4433 -0.1967 -1.4382 3490728.0 0.5664 0.2595 1.0253

D(log(GDP)(-4)) 1.2525 2.0480 -0.0963 -0.9948 0.0611 1.3393 0.0985 0.7872 -1058373.0 -0.1878 -0.5655 -2.4430

D(Trade Balance(-1)) -7.2E-08 -4.1765 -3.7E-09 -1.3606 2.4E-09 1.8292 -5.6E-09 -1.5747 0.0 -0.0330 2.6E-08 3.9091

D(Trade Balance(-2)) -3.0E-08 -1.5995 1.9E-09 0.6286 3.2E-09 2.2829 -4.3E-09 -1.1120 0.3 1.6636 1.2E-08 1.6339



D(log(e)(-1)) -0.2712 -0.5122 0.0780 0.9305 -0.0181 -0.4592 -0.0444 -0.4102 7949543.0 1.6291 -0.1719 -0.8575

D(log(e)(-2)) 0.7701 1.3785 0.1429 1.6163 -0.0359 -0.8630 0.0109 0.0952 3742848.0 0.7271 -0.3572 -1.6897

D(log(e)(-3)) 0.0301 0.0558 0.1020 1.1942 -0.0267 -0.6646 0.0192 0.1743 1183494.0 0.2380 -0.0496 -0.2427

D(log(e)(-4)) 0.1717 0.3324 -0.0263 -0.3215 -0.0290 -0.7529 -0.1193 -1.1294 -4629555.0 -0.9727 0.1639 0.8385

Constant 0.0499 1.8956 0.0015 0.3592 0.0087 4.4265 0.0079 1.4678 -239248.1 -0.9859 0.0032 0.3231

2008 dummy -0.1411 -3.6174 -0.0045 -0.7314 0.0051 1.7644 -0.0161 -2.0148 1135976.0 3.1595 0.0312 2.1146 Critical value for t at the 5% level and 63 df is -1.64 and at the 2.5% is 1.96

Table 9: Vector Error Correction Estimates (Israel)

D(log

(Stock

Index))

t-

statistic

D

(Interest

rate)

t-

statistic

D(log

(CPI))

t-

statistic

D(log

(GDP))

t-

statistic

D(Trade

Balance)

t-

statistic

D(log

(e))

t-

statistic

Adj. Coeff -0.1272 -2.7028 0.0024 0.2758 -0.0014 -0.3479 0.0490 3.2177 -418.4 -1.0102 0.0812 3.2583

Adj. Coeff. 1.3989 1.7456 -1.7070 -11.6059 0.0795 1.1569 -0.1537 -0.5933 -15329.1 -2.1740 -0.9534 -2.2463

D(log(Stock Index)(-1)) 0.1124 0.9476 -0.0134 -0.6149 0.0129 1.2705 -0.0048 -0.1241 2958.9 2.8352 0.0290 0.4609

D(log(Stock Index)(-2)) 0.1495 1.1893 0.0104 0.4504 0.0065 0.6058 0.0196 0.4812 -486.7 -0.4401 -0.1352 -2.0312

D(log(Stock Index)(-3)) 0.0811 0.6790 -0.0008 -0.0384 -0.0006 -0.0620 -0.0200 -0.5187 -1259.6 -1.1985 -0.1001 -1.5818

D(Interest rate(-1)) -0.7828 -1.3060 0.7854 7.1393 -0.0472 -0.9183 0.0638 0.3291 15449.3 2.9294 -0.0834 -0.2628

D(Interest rate(-2)) -0.4224 -0.7811 0.6842 6.8950 -0.0075 -0.1619 0.0842 0.4819 5873.2 1.2345 0.6480 2.2627

D(Interest rate(-3)) -0.4583 -0.8688 0.5806 5.9977 -0.1242 -2.7454 0.0580 0.3399 8285.6 1.7853 0.3325 1.1902

D(log(CPI)(-1)) 0.2316 0.1514 -0.3495 -1.2447 0.4970 3.7863 0.1989 0.4021 -24234.5 -1.8001 0.5984 0.7384

D(log(CPI)(-2)) -0.7059 -0.4589 0.2986 1.0578 -0.1550 -1.1745 -0.2700 -0.5429 -6519.5 -0.4817 0.0269 0.0331

D(log(CPI)(-3)) -2.5864 -1.9100 0.3406 1.3706 0.0716 0.6160 -0.3042 -0.6949 -2675.7 -0.2246 1.7634 2.4588

D(log(GDP)(-1)) -1.6353 -1.9125 -0.1054 -0.6718 -0.0450 -0.6130 -0.1609 -0.5820 -8317.9 -1.1056 1.1792 2.6040

D(log(GDP)(-2)) -1.0108 -1.5497 -0.1022 -0.8534 -0.0324 -0.5785 -0.3821 -1.8117 -7143.0 -1.2446 0.8801 2.5477

D(log(GDP)(-3)) -0.2699 -0.6374 -0.0001 -0.0008 -0.0217 -0.5964 -0.3065 -2.2388 -2829.8 -0.7594 0.3395 1.5137


D(Trade Balance(-2)) 5.0E-06 0.2642 1.2E-05 3.5000 1.7E-06 1.0254 4.1E-06 0.6627 -0.8 -4.5102 3.3E-05 3.2239


D(log(e)(-1)) -0.6420 -2.8666 0.0738 1.7944 0.0199 1.0368 -0.0063 -0.0866 493.1 0.2502 0.2199 1.8540

D(log(e)(-2)) -0.4896 -1.9993 0.0028 0.0633 -0.0249 -1.1864 0.0275 0.3475 1808.7 0.8395 0.0569 0.4385

D(log(e)(-3)) 0.1911 0.8714 0.0361 0.8970 0.0022 0.1172 0.1135 1.6002 1401.0 0.7259 0.0587 0.5052

Constant 0.0266 2.4630 0.0022 1.0914 0.0013 1.3968 0.0085 2.4502 82.4 0.8679 -0.0123 -2.1620

2008 dummy -0.0391 -2.2327 -0.0071 -2.2191 0.0010 0.6901 -0.0015 -0.2666 167.8 1.0902 -0.0037 -0.4027 Critical value for t at the 5% level and 63 df is -1.64 and at the 2.5% is 1.96

Table 10: Likelihood Ratio Test of Weak Exogeneity

Variable Cyprus turkey Israel

LR P-Value LR P-Value LR P-Value

Stock Market

Index 1.050 0.592 8.745 0.013 10.077 0.006

Interest Rate 13.518 0.001 1.366 0.505 8.988 0.011

Cons. Price Index 5.153 0.076 4.069 0.131 1.835 0.399

GDP 2.332 0.312 6.622 0.036 13.799 0.001

Trade Balance 76.222 0.000 88.636 0.000 80.876 0.000

Exchange Rate 3.597 0.166 9.481 0.009 6.082 0.048

macroeconomic variables and stock market volatility: the case … · 2017-10-03 · examined the...

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