A generalized heterogeneous autoregressive model using market information
提出一类新的波动率预测模型，将市场已实现（协）方差和半（协）方差纳入异质自回归框架，实证显示预测精度显著提升，最简模型使股票波动率预测改进9.80个百分点。
This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.