Title: Forecasting the realized variance in the presence of intraday periodicity
Authors: Ana-Maria Dumitru - University of Surrey (United Kingdom)
Marwan Izzeldin - Lancaster University Management School (United Kingdom)
Rodrigo Hizmeri - Lancaster University (United Kingdom) [presenting]
Abstract: The impact of intraday periodicity on forecasting realized volatility is examined by 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.