Forecasting the realized variance in the presence of intraday periodicity
研究了日内周期对已实现波动率预测的影响，发现周期会放大波动率方差并扭曲跳跃估计，进而损害预测效果；为此提出周期调整的HARP模型，基于2000-2016年30只股票和SPY的实证及蒙特卡洛模拟表明，该模型在1天和5天预测期上显著更优。
This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.