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.
Cite this paper
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
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