AFA Ph.D. Student Poster Session at the 2020 Annual Meeting(14)

We provide new evidence on distinct roles of risk and uncertainty in nancial markets through examining trading activity around the U.S. macro news releases. We document a sustained increase in stock and option trading activity coupled with a rise in risk and dramatic drop in uncertainty after the release of Federal Open Market Committee (FOMC) statements. Following the non-FOMC macro news, equity trading activity increases moderately as does risk, while uncertainty remains stable. Comparing the trading activity prior to news release with those in non-event days, we find a signicant reduction in both stock and option trades for FOMC news. For non-FOMC macro news, we report a surge instead in the option trades. Our results suggest that (1) FOMC news help resolve uncertainty, (2) resolution of uncertainty encourages more trading activity than a rise in risk, and (3) investors actively exploit their insights when there is little change in uncertainty.

原文链接：

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression set-up. We consider four intraday measures: the realized volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. In a forecasting study, the set of newly proposed semiparametric models are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized volatility measures, outperform the benchmark models consistently across all indices and various probability levels.