An Empirical Study of the Dynamic Correlation of Japanese Stock Returns

We focus on the pairwise correlations of Japanese stock returns to study their correlation dynamics empirically. Two types of reduced size sample portfolios are created to observe the changes in conditional correlation: a set of individual stock portfolios created by using a network-based clustering algorithm and a single portfolio created from the mean return indexes of the individual sample portfolios. A multivariate GARCH model with dynamic conditional correlation (DCC) is then fitted to the return data of these sample portfolios independently. The estimation results show that the correlation matrices change over time in a way that depends on the sample portfolios; further, the DCC parameters are significantly different between them. Then, the time series of the maximum eigenvalues of the correlation matrices are calculated to observe the changes in correlation intensity. A higher level of correlation intensity is observed during crisis periods, namely after both the Lehman shock and the Great East Japan Earthquake. We also examine the impact of correlation changes on the risk of sample portfolios by using a numerical simulation, with the results showing non-negligible positive impacts. The comparative VaR backtesting simulation also suggests that DCC performs better than CCC.