Tail Risks and Forecastability of Stock Returns of Advanced Economies: Evidence from Centuries of Data

This study examines the out-of-sample predictability of market risks measured as tail risks for stock returns of eight (Canada, France, Germany, Japan, Italy, Switzerland, the United Kingdom (UK), and the United States (US)) 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 since it utilizes the tail distribution rather the whole distribution. 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 relevant model diagnostics such as the Dynamic Quantile test (DQ) test and %Hits to determine the model that best fits the data. The results obtained are then used in the return predictability following the Westerlund and Narayan (2012, 2015) method which allows us to account for some salient features such as persistence, endogeneity and conditional heteroscedasticity effects. We consequently partition our models into three variants (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.