Analysis of volatility transmission mechanism across equity markets

This dissertation presents two studies on volatility transmission mechanism among developed and developing stock markets and U.S. bond market. The first essay investigates the volatility spillovers across developed and emerging stock markets by using dynamic conditional correlations. Throughout the first study to represent developed stock markets, S&P 500 (USA), FTSE-100 (UK), NIKKEI 225 (Japan) indices were used and emerging stock markets were represented by two regional groups. One of them is Asian stock markets namely BSE SENSEX Index (India), IDX (Indonesia), BURSA KLCI (Malaysia), and BIST 100 (Turkey). The other group consists of four major Latin American stock markets MERVAL (Argentina), BOVESPA (Brazil), IPC (Mexico), IPSA (Chile) and COLCAP (Colombia). In this study, spanning the period from 01/01/2002 to 29/02/2016, daily index values of these stock markets were analyzed. In this essay the presence of the structural breaks in variance, long memory property and asymmetry were taken into account. In order to test for long memory property, Rescaled Range Statistics (R/S), Geweke and Porter-Hudak (GPH) Model and Gaussian Semi Parametric (GSP) methods were employed. These test results confirmed that there exists long memory behavior in squared returns of all the stock indices. As a result, FIGARCH specifications seems to be an appropriate modelling framework. The findings also indicated that the relevance of significant asymmetry in the dynamics of stock returns, suggesting that volatility also depends on the sign of unexpected random shock. That is to say, FIAPARCH model allowing for asymmetry in the return and volatility series leads to better fitting. In this study, multivariate dynamic conditional correlation GARCH (DCC-GARCH), dynamic conditional correlation fractionally integrated GARCH (DCC-FIGARCH) and dynamic conditional correlation fractionally integrated asymmetric power autoregressive conditional heteroscedasticity (DCC-FIAPARCH) models were used. Finally, the dynamic conditional correlations were analyzed in order to interpret whether there is an increase in cross-market linkages after a shock or an interdependence across stock markets. Dealing with developed markets, it has been found that there exists higher integration between S&P 500 and FTSE 100. For these markets the correlations are above 0.5 suggesting higher integration between them. On the other hand, on average the correlations between NIKKEI 225 and other markets are less than 0.24. This result indicates that there is portfolio diversification opportunities between NIKKEI 225 and other developed markets. According to estimation results, the evidence has been found that Asian markets are weakly correlated. On average the correlations among Asian stock markets are less than 0.5, whereas the correlations between Malaysia and Indonesia are around 0.4. These results indicate that Asian markets are weakly correlated. Crisis dummies have a significant and positive impact on the dynamic conditional correlations between Asian stock markets, supporting the hypothesis of contagion effect. However, the picture is somewhat different for BOVESPA and MERVAL and BOVESPA and IPC. For these markets the correlations are very high suggesting higher integration between them. The results suggested that diversification opportunities do not seem to exist between BOVESPA and MERVAL, and BOVESPA and IPC. In addition, global financial and Eurozone crisis have positive impact on the DCCs, implying that the exogenous shifts led to a rise in the volatility of the correlations between Latin American stock markets. This finding supports the hypothesis of the contagion effect. In the second essay, the main purpose is to analyze the risk spillovers, "flight to quality" from stock to bond; "flight from quality" from bond to stock and "financial contagion" among the stock exchange and bond markets by using the recently developed econometric techniques. By using the Granger causality tests in moments the "contagion", "flight to quality" and "flight from quality" effects were investigated for BIST 100 (Turkey) and the other financial markets. These tests differ from the Granger causality tests in means and variances in describing the causality in the tails of the distribution and their risk spillovers. Thus it can be determined whether a negative shock to a market influences the other market positively or not. Throughout the study to represent regional stock markets; Standard and Poor's Europe Stock Market Index (SPEU), Standard and Poor's Asia Stock Market Index (SPAS 50) and S&P Africa 40 index were used. National stock markets were represented by BIST 100 (Turkey), S&P 500 (USA), DAX 100 (Germany), FTSE 100 (UK), NIKKEI 225 (Japan), MERVAL (Argentina), BOVESPA (Brazil), IPC (Mexico), IPSA (Chile), COLCAP (Colombia), IDX (Indonesia), S&P BSE SENSEX Index (India), and BURSA KLCI (Malaysia). In order to analyze flight to quality and flight from quality phenomena, causal relationship between these stock markets and U.S. bond market was investigated in this concept. In this study, spanning the period from 01/01/2002 to 29/02/2016 daily index values of these stock markets and the two year U.S. bond yield were analyzed. The results of this study are helpful for analyzing the sensitivity of the risks of the Turkish stock market relative to the other markets over time. They are also helpful to understand whether there is a flight to quality or contagion when the financial markets are exposed to a risk. When stock markets were analyzed as regionally, it may be concluded that there exists a weak contagion from SPEU to BIST 100; a bilateral contagion between S&P Africa 40 and BIST 100; and strong interdependence between SPAS 50 and BIST 100. Furthermore, there exists negative contagion from S&P 500 and FTSE 100 to BIST 100. Moreover, there is a weak contagion from DAX 100 to BIST 100. However, the picture is different for the relationship between BIST 100 and NIKKEI 225. It can be stated that there is a weak volatility spillover from BIST 100 to NIKKEI 225 due to the causality in variance. But in the opposite direction, NIKKEI 225 has no influence on BIST 100. Therefore, one may suggest that NIKKEI 225 and DAX 100 are good diversification alternatives for investors investing in BIST 100. By analyzing causality between BIST 100 and other emerging countries, the evidence shows that there exists negative contagion from IPC and MERVAL to BIST 100. Therewithal, it can be stated that BIST 100 is dependent on BOVESPA and IPSA by the reason of significant Granger causality in the center of the distributions. In addition, one way volatility spillover effect is observed from COLCAP to BIST 100. And for opposite direction, it can be seen that there is one way negative contagion from BIST 100 to IDX. Besides, there exist bilateral negative contagion between BIST 100 and SENSEX. Finally, there is bilateral volatility spillover between BIST 100 and MALAYSIA KLCI. When the relationship between stock market and bond market was analyzed, having said that, there exists contagion from the U.S. bond market to the Japan stock market. The evidence shows that the London stock market index (FTSE 100), German stock market index (DAX 100), and U.S. stock market index (S&P 500) are dependent on the U.S. two year bond yield (USGG2YR). In conclusion only in bear market, when London stock index FTSE 100 is falling, U.S. two year bond yield (USGG2YR) is (weakly) negatively affected. Moreover, during bull market, when London stock index FTSE 100 is rising, U.S. two year bond yield (USGG2YR) is affected positively. After all, one may conclude that BIST 100 is dependent on USGG2YR by the reason of significant Granger causality in the center of the distributions. According to test results, it can be seen that there exists negative contagion from USGG2YR to IDX, and SENSEX. In addition, there exists flight from quality effect from USGG2YR to SENSEX. It may be clarified that there exists weakly flight to quality from MERVAL to USGG2YR. It can be stated that Latin American markets such as IPC, IPSA, and BOVESPA are dependent on U.S. bond market. To sum up, one may conclude that MALAYSIA KLCI and MERVAL are good diversification alternatives for USGG2YR.