The Role of Price‐Volatility Cojumps in Volatility Forecasting
利用高频S&P 500和VIX数据识别日内价格-波动率共同跳跃，构建上下行及非对称指标，嵌入HAR模型后发现下行共同跳跃增加未来波动率、上行共同跳跃降低波动率，且能显著提升预测效果。
ABSTRACT This paper investigates whether simultaneous jumps in prices and volatility improve volatility forecasting. Using up‐to‐date high‐frequency S&P 500 and VIX data, we identify price‐volatility cojumps at the intraday granularity and construct upside, downside, and asymmetric measures. Embedding these into the Heterogeneous Autoregressive (HAR) model, we provide new empirical evidence that downside cojumps increase future volatility, upside cojumps reduce volatility. Out‐of‐sample analysis further shows that incorporating these impacts of cojumps significantly enhances HAR model forecasting performance. Moreover, our results reveal that recent price jumps become important predictors of volatility when accompanied by simultaneous volatility jumps, an effect not previously documented in the literature. Finally, we also document the economic interpretation, policy implications, and economic value of price‐volatility cojumps.