Empirical Modeling the Dynamic Conditional Correlations between Shanghai and Hong Kong Stock Markets

Modelling the volatility of financial time series is essential part of practically oriented financial analysis. Volatility can be interpreted as a measure of riskiness of financial assets. Over past decades, there have been observed volatile market conditions on stock markets. When modeling volatility, univariate volatility models have limited applications only. However, multivariate conditional volatility models allow for model volatility between more time series. The objective of this paper is to model time varying conditional volatility and dynamic conditional correlations between returns of Shanghai Composite and Hang Seng stock indexes and their residuals using the DCC GARCH model. In particular, we consider daily closing returns of those indexes in the period of 1997-2015 years. We identified that dynamic conditional correlations have changed significantly in 2007-2008 years due to the global financial crisis.