Stock Markets Volatility Spillovers during Financial Crises : A DCC-MGARCH with Skew-t Approach

We investigate stock markets volatility spillovers in selected emerging and major developed markets using multivariate GARCH (MGARCH) models [namely; DVECH, CCC-MGARCH, CCC-VARMA-(A)MGARCH, VAR-EGARCH, BEKK-(A)MGARCH, DCC-MGARCH (with Gaussian and t distributions) and DCC-with-skew-t density]. The paper analyses the impacts of recent global financial crisis (2007{2009) on stock market volatility and examines their dynamic interactions using several MGARCH model variants. Structural break detection test (the ICSS algorithm) finds significant evidence of breaks in the unconditional variance for all the stock market returns. Having fitted several MGARCH models, we modify the BEKK-(A)MGARCH models by including financial crisis dummies to assess their impact on stock market volatilities, spillovers and interactions. Major findings reveal that correlations among emerging markets are lower compared with correlations among developed markets and tend to increase during financial crises. Consistent with extant literature, own-volatility spillovers are to a large extent higher than crossvolatility spillovers especially for emerging markets. The DCC-with-skew-t density model is found to have better diagnostics compared to all other fitted MGARCH models partly due to its taking into account the skewed feature of the returns. Thus, we recommend that in modelling stock market volatility dynamics, skewness, asymmetry and fat tails (features frequently observed in financial time series) should be taken into account in the modelling process