Abstract:
This study examines the out-of-sample predictability of market risks measured as tail risks for stock returns of eight advanced countries using a long-range monthly data of over a century. We follow the Conditional Autoregressive Value at Risk (CAViaR) of Engle and Manganelli (2004) to measure the tail risks and consequently, we produce results for both 1% and 5% VaRs across four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR. Thereafter, we use the “best” fit tail risks in the return predictability of the selected advanced stock markets. For the forecasting exercise, we construct three predictive models (one-predictor, two-predictor and three-predictor models) and examine their forecast performance in contrast with a driftless random walk model. Three findings are discernible from the empirical analysis. First, we find that the choice of VaR matters when determining the “best” fit CAViaR model for each return series as the outcome seems to differ between 1% and 5% VaRs. Second, the predictive model that incorporates both stock tail risk and oil tail risk produces better forecast outcomes than the one with own tail risk indicating the significance of both domestic and global risks in the return predictability of advanced countries.
