Financial Turbulence, Systemic Risk and the Predictability of Stock Market Volatility

This paper adds a novel perspective to the literature by exploring the predictive performance of two relatively unexplored indicators of financial conditions, i.e. financial turbulence and systemic risk, over stock market volatility in a sample of seven emerging and advanced economies. The two financial indicators that we utilize in our predictive setting provide a unique perspective to market conditions as they directly relate to portfolio performance metrics from both a volatility and co-movement perspective and, unlike other macro-financial indicators of uncertainty or risk, can be integrated into diversification models within a forecasting and portfolio design setting. Since the two predictors are available at weekly frequency, and we want to provide forecast at the daily level, we use the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) approach. The results suggest that incorporating the two financial indicators (singly and jointly) indeed improves the out-of-sample predictive performance of stock market volatility models across both the short and long horizons. We observe that the financial turbulence indicator that captures asset price deviations from historical patterns does a better job when it comes to the out-of-sample prediction of future returns compared to the measure of systemic risk, captured by the absorption ratio. The outperformance of the financial turbulence indicator implies that unusual deviations in not only asset returns, but also correlation patterns clearly play a role in the persistence of return volatility. Overall, the findings provide an interesting opening for portfolio design purposes in that financial indicators that are directly associated with portfolio diversification performance metrics can also be utilized for forecasting purposes with significant implications for dynamic portfolio allocation strategies.