Dynamic Risk Spillovers from Oil to Stock Markets : Fresh Evidence from GARCH Copula Quantile Regression Based Covar Model

This study proposes a GARCH copula quantile regression model to capture the downside and upside tail dependence between oil price change and stock market returns at different risk levels. In the model, ten copulas are provided to measure the nonlinearity of the tail dependence with the marginal distribution built on the GARCH family models. Using daily price data of stock markets in ten important economies and Brent oil market, we estimate the downward and upward risk spillovers from oil to stock markets. The empirical results suggest strong evidence of risk spillover effects from oil to stock markets. Furthermore, oil has the largest downside and upside risk spillover effects on the Brazilian and Mexican stock markets, respectively. And the US stock market displays the smallest downside and upside risk spillovers from the oil market. We also find evidence that the downside risk spillovers are larger than upside risk spillovers, a finding which is consistent with the flight-to-quality phenomenon. Finally, the dynamic risk spillover effects show heterogeneity over time and are comparatively different for each country. Our results provide significant implications for portfolio managers and international regulators who want to optimize their investment portfolios and maintain stock market stability