C
2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
Bulletin of Economic Research 68:S1, 2016, 0307-3378 DOI: 10.1111/boer.12059
DYNAMIC CORRELATION BETWEEN STOCK RETURNS AND EXCHANGE RATE AND ITS DEPENDENCE ON THE CONDITIONAL VOLATILITIES – THE CASE OF SEVERAL EASTERN EUROPEAN COUNTRIES ˇ Dejan Zivkov, Jovan Njegi´c and Jasmina Pavlovi´c Novi Sad Business School, Novi Sad, Serbia
ABSTRACT The objective of the paper is to determine whether the linkage between stock returns and exchange rates in several Eastern European countries was in accordance with the flow oriented model or the portfolio-balance approach. The dynamic interdependence between exchange rate and stock returns is determined using the Dynamic Conditional Correlation (DCC) framework. The results pointed to a negative dynamic correlation which is in line with portfolio-balance approach. Rolling regression revealed that conditional correlation was affected primarily by conditional volatility of currency, while the impact of stock returns volatility was negligible. Keywords: Dynamic Conditional Correlation; East European markets; exchange rate; stocks; rolling regression JEL classification numbers: C51, F31, G12
I. INTRODUCTION
Financial globalization made Eastern European emerging markets more accessible to international investors, whereby huge volumes of entering capital influenced the demand/supply for both domestic stocks and domestic currencies. In such circumstances, stock returns and exchange rate dynamics have to be mutually intertwined in some extent (Kanas, 2000). If international equity investors perform unhedged regarding currency changes, one of their concerns is exchange rate exposure. Thus, their interest is to evaluate the stability of the foreign exchange markets in order to avoid biased judgments. Taking into account that currency appreciation means negative returns and vice-versa, a negative correlation between exchange rate and equity returns could magnify the volatility of both variables, while positive relationship may offset overall risk. As Greenwood (2005) asserted, the two strands of classical theories, that try to explain the interconnection between stock prices and exchange rates changes, are the flow oriented model and Correspondence: Jovan Njegi´c, Novi Sad Business School, Vladimira Peri´ca Valtera 4, 21000 Novi Sad, Serbia. Email:
[email protected]
S28
Dynamic Correlation Between Stock Returns and Exchange Rate
S29
the portfolio-balance approach. The flow oriented model asserts that appreciation/depreciation of domestic currency decreases/increases the international competitiveness which eventually influences the balance of trade position as well as country’s output. Higher/lower cash flows of companies are consequently transferred to the higher/lower values of stock prices. This stance focuses on the current account of the country’s trade balance and advocates a positive correlation between two variables. On the other hand, portfolio-balance approach accentuates country’s capital account and demand/supply on the stock markets as well as currency market. It predicts that currency depreciation will cause lower demand of domestic stocks which eventually diminishes their value, while currency appreciation will render the opposite effect. Consequently, portfolio-balance approach advocates negative correlation between these categories. This paper analyses the dynamic interdependence between exchange rate and stock returns in four major East European emerging countries (the Czech Republic, Hungary, Poland and Russia) which did not conduct the policies of the fixed exchange rate in the observed period. Some of those countries have led a de facto flexible exchange rate (the Czech Republic and Poland), while Hungary has pursued a fixed regime with wide bands. The Russian currency was characterized by tight management until 2008, followed by greater flexibility afterwards. The paper has a three-fold contribution. Firstly, it supplements the existing literature on this topic. As claimed by Ulku and Demirci, (2012), there is a lack of research conducted on correlation between stock markets and exchange rates in European emerging markets. Secondly, the analysis is done by using Dynamic Conditional Correlation (DCC) multivariate GARCH models developed by Engle (2002), which could indicate a more direct interdependence between these two assets. This particular approach allows the correlations to change over time, utilizing the flexibility of univariate GARCH but without the perplexity of conventional multivariate GARCH. It implies computational advantages of DCC model over multivariate GARCH models, meaning that the number of parameters to be estimated in the correlation process is independent of the number of series to be correlated, Engle (2002). As Milani and Ceretta (2014) asserted, correlation is perhaps the most traditional way of measuring the association between two variables which affect the portfolio assemblage. Also, DCC methodology could gauge a contagion effect as the general process of shock transmission across markets. Finally, as far as we know, this paper is the first one that utilized rolling regression of the conditional correlation on the conditional volatilities of exchange rate and stock returns in this group of countries. This particular approach further sheds light on the factors determining conditional correlation before, during and after the 2007–09 crises. The paper is organized as follows. Section 2 considers theoretical background. Sections 3 and 4 describe methodological approach and data description. Sections 5 and 6 present research results of the DCC estimation and rolling regression with the conclusion in section 7.
II. LITERATURE REVIEW
The extant literature about the nexus between the exchange rate and the stock returns is abundant, but there are conflicting findings in terms of its direction. Both mentioned theories have viable assumptions but one important prerequisite considers whether the causality is observed in the short or long-term. In the short-term, the portfolio-balance approach usually has precedence while in the long-term it is flow oriented theory. Some papers have endeavoured to determine long-run relationship but majority of the existing studies have investigated short-run connection. As Caporale and Pittis (1997) asserted, the accuracy of long-run relationship is harder to determine due to the fact that inferences about the long-run relationship are invalid or nonexistent in an incomplete system, i.e., if an important variable is omitted. For instance, Alagidede et al. C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
S30
Bulletin of Economic Research
(2011) examine the causality between exchange rates and stock prices in Australia, Canada, Japan, Switzerland and U.K. in a linear and non-linear framework and determined that there is no long-run relationship between the two variables. However, exploring the short-run causality the authors found linkage running from exchange rate to stock prices in Canada, Switzerland and U.K. and the opposite direction in Switzerland. Similar conclusions were presented by Lean et al. (2011) while inspecting the connection between exchange rates and stock prices in eight Asian countries. The authors checked for cointegration and Granger causality for individual countries using the Gregory and Hansen cointegration test. The results indicated no long-run relation, only a contemporaneous effect which is reflected in the short-run. Nevertheless, some authors found long-run connection which stands in line with flow-oriented hypothesis. For instance, Phylaktis and Ravazzolo (2005) study on a group of Pacific Basin countries (Hong Kong, Malaysia, Singapore, Thailand and Philippines) found evidence of positive correlation between stock and foreign exchange markets in the long-run. Matsubayashi (2011) using VAR model obtained some interesting results favouring flow-oriented model in Japan. The author found evidence that depreciation of the Japanese yen has some effect in the stimulation of investment in the manufacturing industry and a less prominent effect in the non-manufacturing industry. Bartram and Bodnar (2012) focused on estimating exchange rate exposures analyzing a large sample of non-financial firms from 37 countries around the world. The authors argued that the impact of exchange rates on returns must predominantly, if not exclusively, be an effect on the cash flows of a firm. The study of Diamandis and Drakos (2011) tested the short-run and long-run dynamics between stock and foreign exchange markets for four Latin American countries (Argentina, Brazil, Chile and Mexico), as well as their relation with the U.S. stock markets. They stipulated that the two markets in these economies are positively related and that the U.S. stock market represents a funnel for these links. Short-run connection in accordance with portfolio-balance theory was found in the following papers. Fang (2002) investigated the effects of currency depreciation on stock returns in the shortrun for five East Asian economies: Hong Kong, South Korea, Taiwan, Singapore and Thailand. He found a significantly negative relation between the equity returns and the depreciation rate in the stock markets of Singapore, Taiwan, South Korea and Thailand but not in the market of Hong Kong. The empirical findings indicated that domestic currency depreciation not only decreases the mean stock return but also increases stock market volatility. Liang et al. (2013) backed the portfolio-balance hypothesis utilizing the panel Granger causality and panel DOLS methodologies on ASEAN-5 countries. They claimed that exchange rates impact stock prices negatively via capital mobility. Walid et al. (2011) employed a Markov-Switching EGARCH model to investigate the dynamic linkage between stock prices volatility and exchange rate for four Asian emerging markets. They found support for portfolio-balance approach because the negative sign of exchange rate parameters in the mean equation leads to the conclusion that currency depreciation reduces stock market returns in all four countries. The study of Tai (2007) disclosed that the dynamic relationship between stock market and foreign exchange market is coherent with the stock-oriented exchange rate model for six Asian countries. Numerous research papers exploring emerging Asian countries confirmed portfolio-balance theory probably because these countries have been attracting huge amount of foreign capital in recent decades and such vast capital inflows have been affecting the volatility of exchange rate markets and stock markets as well. Similar assumption could be viable for emerging East European countries (EEC) as they get more integrated into the major financial markets and become more tempting for foreign capital. The study of Moore and Wang (2014) investigates the sources of the dynamic relationship between real exchange rates and stock return differentials using the dynamic conditional correlation (DCC) methodology on several developed and emerging markets. They consider two C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
Dynamic Correlation Between Stock Returns and Exchange Rate
S31
main driving forces, economic integration and financial coherency. The former is associated with international competitiveness (flow oriented theory) while the latter is connected with international portfolio behaviour (portfolio-balance approach). The empirical results revealed that economic integration is likely to be the main force of the linkage between stock markets and exchange rate markets in countries with a relatively low degree of capital mobility. In countries with high capital mobility, financial integration is the main cause why exchange rate influence stock returns. Additionally, Eichler and Maltritz (2011) argued that emerging economies with high inflows of portfolio capital invested in their booming stock markets are especially prone to stock market induced currency crises, whereas countries with modest capital inflows and stagnating or falling stock markets are not.
III. METHODOLOGICAL APPROACH
There are several reasons why a DCC technique is utilized in the paper. Firstly, DCC methodology demonstrates a more straightaway indication of interdependence between stock and foreign exchange markets compared to unconditional rolling correlation. DCC approach couples the correlation dynamics along with the volatility of the series. As Forbes and Rigobon (2002) discussed, the cross market correlation coefficients are conditional on market volatility, and if it is not adjusted for heteroscedasticity, the estimated correlation coefficients could be biased. Secondly, data sample used in the paper comprises 2007–09 world economic turmoil and the DCC approach could help in gaining insight as to whether or not dynamic correlations between two markets behave differently during crisis periods compared with tranquil ones. As Climent and Meneu (2003), and Guo et al. (2011) asserted, the correlation between financial markets tending to be higher during the crisis, which is accompanied by lower returns and greater volatility. For instance, Lin (2012) investigated the co-movement between exchange rates and stock prices in the Asian emerging markets and determined that the connection between exchange rates and stock prices becomes stronger during crisis periods than in tranquil ones. A similar conclusion was derived by Inci and Lee (2014) who examined the relation between stock returns and exchange rate in eight major developed markets. They concluded that dynamic relation has been more significant and stronger in recession periods than in expansion periods. In this research, a contagion impulse is investigated which is defined as process of shock transmission across the markets. This effect could be the consequence of asymmetric information resulting from changes in asset correlation coefficients, Wang and Lai (2013). Many authors, inter alia, Longin and Solnik (2001); Mun (2007); Chang and Su (2010) found asymmetric volatility in the conditional variances of financial assets. Assuming presence of the asymmetric effect in the selected East European asset markets, a bivariate EGARCH(1,1)-DCC model is applied. We tried different lag parameters of EGARCH model, but we chose the (1,1) lag parameters in EGARCH specification due to the fact that this particular model outperformed all the other tested EGARCH models. The decision is made based on Schwarz information criterion (SIC). Since the main purpose of the research is to examine the dynamic correlations between stock returns and exchange rates separately for each country, we applied independent bivariate EGARCH(1,1)-DCC models instead of a unique multivariate EGARCH model. Also, the advantage of this model is the ability to handle very large correlation matrices. The mean equation of the specification considering pair-wise relationships between corresponding stock returns, ri , and exchange rate, ei , is given by: yi,t = ci,t + εi,t ;
εi,t = h i,t z i,t ;
z i,t ∼ iid
C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
(1)
S32
Bulletin of Economic Research
where, y is the 2×1 vector containing the stock returns and exchange rate, yi,t = [ri,t , ei,t ] . Stock returns and exchange rates are calculated as ri,t = 100 × log(Pi,t /Pi,t−1 ) and ei,t = 100 × log(F X i,t /F X i,t−1 ); Pi,t is the stock closing price for the particular stock (i) at time (t). FX is nominal exchange rate of currency (i) compared to euro at time (t). εt = [ε1t , ε2t ] is the vector of innovations. Common white noise is presented by zi,t conditional on the information (I) at time t-1, which can follow Normal, standard Student t or Generalized Error (GED) distributions z i,t |It−1 ∼ [N (0, 1); St(0, 1, υ); G E D(0, 1, k)]. Parameter υ denotes the degrees of freedom, measuring the degree of fat-tails of the residuals density. c is 2×1 vector of constants, and k parameter controls skewness. Symbol i labels particular country. After preliminary tests, the Student t distribution is selected for the research. The univariate EGARCH(1,1) processes for both assets, r and e, in the DCC specification have the following form: ε εt−1,i t−1,i (2) ln(h t,i ) = αi + βi ln(h t−1,i ) + γi + δi h t−1,i h t−1,i where, hi,t is a conditional variance of the asset i in period t. γ is a constant term, β term captures the persistence of volatility, γ gauges an ARCH effect. δ is the coefficient that measures asymmetric response of volatility to positive and negative shocks. Following Aielli (2013) the vector εt has the conditional covariance matrix: Ht = Jt1/2 Ct Jt1/2
(3)
where, Jt = diag{h 1,t , . . . , h N ,t } is a 2×2 diagonal matrix which follow the time varying conditional variance from univariate EGARCH process as diagonal element. Ct ≡ [ρi j,t ] is the asset correlation matrix containing conditional correlation coefficients, modelled as a function of the past standardized returns: Q t Q ∗−1 Ct = Q ∗−1 t t
(4)
∗ t
where, Q represents a 2×2 diagonal matrix with the square root of the i-th diagonal element of Q t on its i-th diagonal position. By construction, the evolution of conditional correlation in the DCC model is presented as: ¯ − a − b) + aνt−1 νt−1 + bQ t−1 (5) Q t = Q(1 ¯ is just a pawhere, asset-return residuals are standardized, i.e., νt = [ε1,t / h 1,t , ε2,t / h 2,t ] . Q rameter affecting the mean of the correlation process and a and b are the non-negative scalar parameters that satisfy 0 < a + b < 1. This process is mean reverting but conditional correlation becomes integrated when the sum equals 1. In order to ensure a conditional correlation between −1 and +1 the value Qt is normalized using ρi j = qi j,t (qii,t q j j,t )−1/2 ; i, j = 1, 2. All parameters are estimated with the Maximum Likelihood procedure using Broyden-FletcherGoldfarb-Shanno (BFGS) optimization algorithm.
IV. DATA DESCRIPTION
Data set used in the paper encompasses daily observations for five East European stock indices – WIG (Warsaw SE), PX (Prague SE), BUX (Budapest SE) and RTS (Moscow SE) as well as corresponding currencies – zloty, koruna, forint and ruble. The selected period ranges from 1 January 2002 to 31 March 2014 and the data are gathered from Datastream International. The nominal exchange rates are observed relative to the euro and the national stock indices are used in local currency terms based on daily closing prices. Due to the unavailability of C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
Dynamic Correlation Between Stock Returns and Exchange Rate
S33
TABLE 1 Descriptive statistics of weekly exchange rate and stock returns
Mean
Stan. Dev. Skewness Kurtosis
Indices BUX 0.032 WIG 0.045 PX 0.033 RTS 0.054 Currencies Forint 0.008 Zloty 0.006 Koruna −0.005 Ruble 0.020
JB
LB(Q) LB(Q2 )
ZA test
1.661 1.348 1.483 2.251
−0.249 −0.323 −0.419 −0.737
8.307 6.538 10.563 16.831
3336.51 1518.35 6797.90 22717.02
0.010 0.291 0.000 0.000
0.000 0.000 0.000 0.000
−18.028* −50.491* −17.731* −10.811*
0.660 0.637 0.444 0.540
0.288 0.301 −0.799 0.527
14.948 7.863 25.921 8.509
16801.72 2819.19 61971.26 3694.03
0.002 0.000 0.326 0.000
0.000 0.000 0.000 0.000
−26.791* −10.186* −23.534* −17.061*
Notes: JB stands for Jarque-Bera coefficients of normality, LB-Q and LB-Q2 test denote p-values of LjungBox Q-statistics for level and squared residuals up to 36 lags, ZA-test stands for Zivot-Andrews unit-root test with 20 lags, asterisk label one percent significance level for ZA-tests.
TABLE 2 SIC values for different EGARCH(p,q)-DCC specifications
EGARCH(1,1) EGARCH(2,1) EGARCH(1,2) EGARCH(2,2)
Czech Republic
Hungary
Poland
Russia
3.8290 3.8327 3.8351 3.8348
5.0331 5.0332 5.0414 5.0479
4.5235 4.5276 4.5273 4.5309
5.1870 5.1851 5.1936 5.1936
some data because of national holidays, the daily dates are synchronized between two markets according to existing observations. Table 1 presents a succinct summary statistics, i.e., first four moments, Jarque-Bera coefficient, Ljung-Box Q-statistics tests for level and squared residuals and Zivot-Andrews (1992) unit-root test, which is robust to the existence of structural breaks. Positive mean values of stock returns indicate that investors gained on average positive results. Negative skewness points to more negative data concentrated around the mean value for all indices and Koruna, while for all other assets the opposite applies. Positive mean values of observed currencies indicate that all currencies on average depreciate, while Koruna appreciates in the observed period. The findings also suggest that all series express significant erratic behaviour and non-normal features, which is corroborated by large excess kurtosis and JarqueBera coefficients. Fat tails and more peaked mean indicates that extreme changes occurs more frequently, thus every EGARCH-DCC model is estimated with both normal distribution and Student-t distribution. According to SIC, Student-t distribution proved to be more suitable one. For almost all observed series, the LB-Q tests found an autocorrelation presence and the LB-Q2 statistics propound the presence of time varying-variance in all series, showing clear evidence of an ARCH pattern. It indicates that GARCH parameterization might be appropriate for the conditional variance processes. ZA test with 20 lags suggests that all series are stationary. We tried different lag orders in EGARCH(p,q)-DCC specifications. The decision regarding the optimal model is made based on SIC values presented in Table 2. According to these values, EGARCH(1,1)-DCC is the best fitted model for Poland, Hungary and Czech Republic, while in C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
S34
Bulletin of Economic Research
TABLE 3 Estimated results from the bivariate EGARCH(1,1)-DCC model
Stock returns (ri )
The Czech Republic PX
Hungary BUX
Poland WIG
Russia RTS
c α β γ δ LB(Q) LB(Q2 ) Exchange rate (ei )
0.088* −0.154* 0.968* 0.220* −0.049* 0.112 0.600 Koruna
0.042*** −0.106* 0.979* 0.159* −0.045* 0.301 0.498 Forint
0.078* −0.096* 0.984* 0.135* −0.044* 0.706 0.580 Zloty
0.151* −0.117* 0.975* 0.199* −0.047* 0.066 0.965 Ruble
c α β γ δ LB(Q) LB(Q2 ) DCC parameters
−0.005 −0.109* 0.992* 0.126* −0.004 0.770 1.000
−0.005 −0.145* 0.979* 0.161* 0.063* 0.822 1.000
−0.015*** −0.167* 0.978* 0.183* 0.063* 0.677 0.441
0.013*** −0.118* 0.989* 0.138* 0.036* 0.414 0.463
0.013* 0.976* 6.641*
0.019* 0.962* 7.013*
0.017* 0.975* 7.701*
0.016* 0.979* 6.721*
a b υ
Notes: LB-Q and LB-Q2 test denote p-values of Ljung-Box Q-statistics for level and squared residuals up to 45 lags. *, **, *** represent statistical significance at the 1%, 5% and 10% level, respectively.
case of Russia, SIC proposed EGARCH(2,1). However, the model failed to converge, for which reason we chose the second best, i.e., EGARCH(1,1).
V. RESEARCH RESULTS
Table 3 lists the parameters of the dynamic correlation between stock returns and exchange rate as well as diagnostic tests. For all assets, β and γ coefficients are highly statistically significant. Level of β parameter implies high persistence of the log conditional variance process in all asset markets. The asymmetric parameter (δ) is negative and significant for all indices, while positive and significant for all currencies but Koruna. It points to a leverage effect, meaning that negative shocks have more pronounced effect than positive shocks in the equity markets. As for currencies, it indicates that depreciation has more profound effect than appreciation, which is expected. All EGARCH models have very sound statistical adequacy as pointed by the Ljung-Box diagnostic tests, proving the absence of serial correlation and heteroscedasticity. The conditional correlation of DCC also exhibit high persistence in all cases examined. This effect is verified by the a and b parameters, significant at above 1 percent level, revealing a substantial time varying co-movement. The sum of a and b parameters in all cases meet the restriction C 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley & Sons Ltd
S35
Dynamic Correlation Between Stock Returns and Exchange Rate .4
.2
Czech Republic
.2
.0
.0
-.2
-.2
-.4
-.4
-.6
-.6 .4
02
03
04
05
06
07
08
09
10
11
12
13 14
-.8
.0
Russia
Poland
02
03
04
05
06
07
08
09
10
11
12
13 14
04
05
06
07
08
09
10
11
12
13 14
Hungary
-.1
.2
-.2
.0
-.3
-.2
-.4
-.4 -.6
-.5
02
03
04
05
06
07
08
09
10
11
12
13 14
-.6
02
03
Fig. 1. Estimated conditional correlation coefficients.
a + b
