Overnight News and Daily Equity Trading Risk Limits
提出一种利用高频数据设定每日股票交易风险限额的双变量建模方法，通过考虑隔夜与日内收益的不同动态及其协方差来预测次日风险价值，并用标普500和罗素2000指数数据验证了协方差建模对交易量较小的小盘股更有效。
This article proposes a new bivariate modeling approach for setting daily equity-trading risk limits using high-frequency data. We construct one-day-ahead Value-at-Risk forecasts by taking into account the different dynamics of the overnight and daytime return processes and their covariance. The covariance is motivated by market microstructure effects such as price staleness and news spillover. Among the competitors we include a simpler bivariate model where the overnight return is redefined by moving the open price further into the trading day, and a univariate model based on the close-to-close return and an overnight-adjusted realized volatility. We illustrate the different approaches using data on the S&P 500 and Russell 2000 indices. The evidence in favor of modeling the covariance is more convincing for the latter index because of the lower trading volumes and, relatedly, the less efficient price discovery at market open for small-cap stocks.