Forecasting Realized Volatility Using Subsample Averaging

Huiyu Huang; Tae-Hwy Lee

doi:10.4236/ojs.2013.35044

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Open Journal of Statistics > Vol.3 No.5, October 2013

Forecasting Realized Volatility Using Subsample Averaging ()

Huiyu Huang, Tae-Hwy Lee

Department of Economics, University of California, Riverside, USA.
GMO Emerging Markets Equity, Berkeley, USA.

DOI: 10.4236/ojs.2013.35044 PDF HTML 3,312 Downloads 5,094 Views Citations

Abstract

When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.

Keywords

Subsample Averaging; Forecast Combination; High-Frequency Data; Realized Volatility; ARFIMA Model; HAR Model

Share and Cite:

H. Huang and T. Lee, "Forecasting Realized Volatility Using Subsample Averaging," Open Journal of Statistics, Vol. 3 No. 5, 2013, pp. 379-383. doi: 10.4236/ojs.2013.35044.

Conflicts of Interest

The authors declare no conflicts of interest.

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