Forecasting Realized Volatility Using Subsample Averaging

Tae Hwy Lee and Huiyu Huang (evelyn.huang@gmo.com)
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Huiyu Huang: Grantham, Mayo, Van Otterloo and Company LLC

No 201410, Working Papers from University of California at Riverside, Department of Economics

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 the subsample-averaging has been proposed and used in estimating RV, this paper is the first that uses the 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, that generates forecasts from each subsample, and then combine these forecasts. We find that, in daily S&P 500 return RV forecasts, subsample-averaging generates better forecasts than those using only one subsample without averaging over all subsamples.

Keywords: Subsample; averaging.; Forecast; combination.; High-frequency; data.; Realized; volatility.; ARFIMA; model.; HAR; model. (search for similar items in EconPapers)
JEL-codes: C53 C58 G17 (search for similar items in EconPapers)
Pages: 9 Pages
Date: 2014-09
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ger and nep-mst
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Published in Open Journal of Statistics 3(5): 379-383. October 2013.

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