Increasing the Information Dynamics of Realized Volatility Forecasts

Abstract

This paper draws upon several distinct contributions to improve the out-of- sample forecasting performance of realized volatility models. More specifically, we retain the rolling-sample idea of Andreou and Ghysels (2002) to propose a new approach we call the Rolling Realized Volatility (RRV ), which samples consecutive high-frequency squared returns regardless of whether they originate from the same trading session like in the traditional approach. This new approach yields a sample approximately M times larger than the traditional approach, where M is the intraday sampling frequency. The new approach has at least two advantages. First, having more observations increases the informational dynamics of the OLS regression. Second, the Rolling method accounts for the serial correlation between the last returns in day t − 1 and the first returns in day t. We test competing out-of-sample forecast losses from the new approach against those of the traditional method for the S&P 500 and 26 Dow Jones Industrial Average stocks. Using several state-of-the-art realized volatility models, both a simulation and an empirical exercise strongly suggest the Rolling approach yields superior out-of-sample performance over the traditional approach.