Sang Hoon Kang

Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets

ABSTRACT This article investigates the asymmetric and long memory volatility properties and dynamic conditional correlations (DCCs) between Brazilian, Russian, Indian, Chinese, and South African (BRICS) stock markets and commodity (gold and oil) futures markets, using the trivariate DCC-fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) model. We identify significant asymmetric and long memory volatility properties and DCCs for pairs of BRICS stock and commodity markets, and variability in DCCs and Markov Switching regimes during economic and financial crises. Finally, we analyze optimal portfolio weights and time-varying hedge ratios, demonstrating the importance of overweighting optimal portfolios between BRICS stock and commodity assets.

Dynamic Global Linkages of the BRICS Stock Markets with the United States and Europe Under External Crisis Shocks: Implications for Portfolio Risk Forecasting

Crisis shocks often lead to changes in the interdependence across stock markets, and thus risk assessment and management. This paper investigates the extent to which the global financial crisis of 2008-2009, which was triggered by the US subprime crisis in 2007, and the European debt crisis started at the end of 2009, affect the interdependence of the leading emerging markets of the BRICS countries with those of the United States and Europe. Our empirical analysis makes use of the FIAPARCH model combined with the Dynamic Equicorrelation (DECO-FIAPARCH), which allows for the estimation of market linkage for a large group of countries as a whole, while controlling for asymmetric volatility and long memory. The results reveal the presence of important changes in the time-varying linkages of the BRICS stock markets with the US and European ones. In particular, the average linkages have significantly been higher between 2007 and the first half of 2012 than the remaining part of the sample, and there is also evidence of structural change around the Lehman Brothers collapse. We also show the effects of these stylized facts on portfolio risk assessment and forecasting.

Long Memory Features Evolve in the Time-varying Process in Asia-pacific Foreign Exchange Markets☆

Abstract We investigated the presence of, and changes in, long memory features in the returns and volatility dynamics of six Asia-Pacific foreign exchange markets (Australian dollar, Japanese yen, Korean won, New Zealand dollar, Singaporean dollar, and Taiwan dollar) using time-varying Hurst exponents. In particular, instead of relying on a single static measure of long memory, we explored time-varying long memory features over time to assess changes in market efficiency by analyzing the returns and volatility of the markets. Furthermore, considering a time-varying rolling approach, we estimated values of the Hurst exponent for time windows with 1,000 observations (about 4 years of data) in each window. The estimation results indicated that both the returns and the volatility series possessed strong long memory features and that the degree of the long memory features changed over time. Additionally, the Hurst exponent showed an upward trend during the 1997 Asian currency crisis and 2008 global financial crisis, indicating that exchange rate markets became inefficient and predictable during the financial crisis.

Dynamic connectedness network in economic policy uncertainties

ABSTRACT This study investigates the dynamic connectedness across nine economic policy uncertainty indexes. Our results indicate that the total spillover index is on average 67.4%, indicating a high level of interconnectedness across the nine indexes. In particular, the EU is the largest transmitter of uncertainty connectedness. In addition, China becomes a net transmitter of connectedness during the global financial crisis and European debt crisis. This finding indicates that the uncertainty of Chinese economic policy is an important contributor to the connectedness of the uncertainty network.

Information Transmission of Volatility between WTI and Brent Crude Oil Markets

ABSTRACT : Transmission mechanisms of volatility between two crude oil markets (WTI and Brent markets) have drawn the attention of numerous academics and practitioners because they both play crucial roles in portfolio and risk management in crude oil markets. In this context, we examined the volatility linkages between two representative crude oil markets using a VECM and an asymmetric bivariate GARCH model. First, looking at the return transmission through the VECM test, we found a long-run equilibrium and bidirectional relationship between two crude oil markets. However, the estimation results of the GARCH-BEKK model suggest that there is unidirectional volatility spillover from the WTI market to the Brent market, implying that the WTI market tends to exert influence over the Brent market and not vice versa. Regarding asymmetric volatility transmission, we also found that bad news volatility in the WTI market increases the volatility of the Brent market. Thus, WTI information is transmitted into the Brent market, indicating that the prices of the WTI market seem to lead the prices of the Brent market.Keywords : Asymmetric volatility transmission, Causality, Cointegration, GARCH-BEKK model, Volatility spillover effectJEL 분류 : C32, C58, G14, G17, Q47

Modeling and Forecasting the Volatility of Eastern European Emerging Markets

This study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, naThis study has attempted to seek a volatility forecasting model that can reflect sufficiently the long memory characteristic in the volatility of four Eastern European emerging stock markets, namely, Hungary, Poland, Russia, and Slovakia. From the results of our empirical analysis, we found that the FIGARCH model is better equipped to capture the long memory property in the volatility of these markets than the GARCH and IGARCH models. More importantly, the FIGARCH model is found to provide superior performance in one-day-ahead volatility forecasts. Thus, this study recommends researchers, portfolio managers, and traders to use the long memory FIGARCH model in analyzing and forecasting the volatility dynamics of Eastern European emerging markets.