Modeling ex post variance jumps: implications for density and tail risk forecasting
研究了多种时间依赖的事后方差跳跃模型，发现其能显著改善多期方差密度预测和尾部风险预测，对风险管理和资产定价有实际价值。
This paper focuses on modeling ex post variance jumps including several time-dependent arrival specifications to assess their importance to forecasts of daily returns and variance measures. The benchmark specification for variance measures includes two autoregressive components that capture the persistent and transitory elements. To this we add a jump process with either independent arrival rates, autoregressive conditional jump intensities, or a stochastic autoregressive jump arrival specification. Results from four major markets and four stocks show that ex post variance jumps are frequent and persistent. Modeling time-dependent variance jumps strongly improves ex post variance density forecasts for multiperiod forecast horizons and improves forecasts of the return density. There are economic benefits to modeling variance jumps as well. Models with time-dependent ex post variance jumps improve tail risk forecasting of value-at-risk and expected shortfall.