This paper investigates the predictability of economic policy uncertainty (EPU) to stock market volatility. Our in-sample evidence suggests that higher EPU leads to significant increases in market volatility. Out-of-sample findings show that incorporating EPU as an additional predictive variable into the existing volatility prediction models significantly improves forecasting ability of these models. The improvement is robust to the model specifications.

In this paper, we investigate the impacts of oil price shocks on the bilateral exchange rates of the U.S. dollar against currencies in 16 OECD countries. Our empirical findings indicate that the responses of dollar exchange rates to oil price shocks differ greatly depending on whether changes in oil prices are driven by supply or aggregate demand. Oil price shocks (need to say supply or demand shocks here) can explain about 10%-20% of long-term variations in exchange rates. The explanatory ability of oil shocks to exchange rate variations becomes much greater after global financial crisis. Based on parametric and nonparametric tests, we find little evidence of nonlinear relations between oil prices and exchange rates.

In this study we propose several new variables, such as continuous realized semi‐variance and signed jump variations including jump tests, and construct a new heterogeneous autoregressive model for realized volatility models to investigate the impacts that those new variables have on forecasting oil price volatility. In‐sample results indicate that past negative returns have greater effects on future volatility than that of positive returns, and our new signed jump variations have a significantly negative influence on the future volatility. Out‐of‐sample empirical results with several robust checks demonstrate that our proposed models can not only obtain better performance in forecasting volatility but also garner larger economic values than can the existing models discussed in this paper.

No abstract

No abstract