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Forecasting Realized Volatility Using Subsample Averaging

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DOI: 10.4236/ojs.2013.35044    3,085 Downloads   4,822 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.

Conflicts of Interest

The authors declare no conflicts of interest.

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.

References

[1] T. G. Andersen, T. Bollerslev, F. X. Diebold and P. Labys, “The Distribution of Realized Exchange Rate Volatility,” Journal of the American Statistical Association, Vol. 96, No. 453, 2001, pp. 42-55. http://dx.doi.org/10.1198/016214501750332965
[2] O. E. Barndorff-Nielsen and N. Shephard, “ Econometric Analysis of Realised Volatility and Its Use in Estimating Stochastic Volatility Models,” Journal of the Royal Statistical Society Series B, Vol. 64, No. 2, 2002, pp. 253280.
[3] Y. Ait-Sahalia, P. A. Mykland and L. Zhang, “How Often to Sample a Continuous-time Process in the Presence of Market Microstructure Noise,” Review of Financial Studies, Vol. 18, No. 2, 2005, pp. 351-416. http://dx.doi.org/10.1093/rfs/hhi016
[4] F. M. Bandi and J. R. Russell, “Microstructure Noise, Realized Variance, and Optimal Sampling,” Review of Economic Studies, Vol. 75, No. 2, 2008, pp. 339-369.
http://dx.doi.org/10.1111/j.1467-937X.2008.00474.x
[5] P. R. Hansen and A. Lunde, “Realized Variance and Market Microstructure Noise,” Journal of Business and Economic Statistics, Vol. 24, No. 2, 2006, pp. 127-218.
http://dx.doi.org/10.1198/073500106000000071
[6] L. Zhang, P. A. Mykland and Y. Ait-Sahalia, “A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data,” Journal of the American Statistical Association, Vol. 100, No. 472, 2005, pp. 1394-1411. http://dx.doi.org/10.1198/016214505000000169
[7] O. E. Barndorff-Nielsen, P. R. Hansen, A. Lunde and N. Shephard, “Designing Realised Kernels to Measure the Expost Variation of Equity Prices in the Presence of Noise,” Econometrica, Vol. 76, No. 6, 2008, pp. 14811536. http://dx.doi.org/10.3982/ECTA6495
[8] O. E. Barndorff-Nielsen, P. R. Hansen, A. Lunde and N. Shephard, “Subsampling Realised Kernels,” Journal of Econometrics, Vol. 160, No. 1, 2011, pp. 204-219.
http://dx.doi.org/10.1016/j.jeconom.2010.03.031
[9] B. Zhou, “High-Frequency Data and Volatility in Foreign-Exchange Rates,” Journal of Business and Economic Statistics, Vol. 14, No. 1, 1996, pp. 45-52.
[10] T. G. Andersen, T. Bollerslev, F. X. Diebold and P. Labys, “Modeling and Forecasting Realized Volatility,” Econometrica, Vol. 71, No. 2, 2003, pp. 579-625.
http://dx.doi.org/10.1111/1468-0262.00418
[11] F. Corsi, “A Simple Approximate Long Memory Model of Realized Volatility,” Journal of Financial Econometrics, Vol. 7, No. 2, 2009, pp. 174-196. http://dx.doi.org/10.1093/jjfinec/nbp001
[12] J. Geweke and S. Porter-Hudak, “The Estimation and Application of Long Memory Time Series Models,” Journal of Time Series Analysis, Vol. 4, No. 4, 1983, pp. 221-238.
http://dx.doi.org/10.1111/j.1467-9892.1983.tb00371.x
[13] A. J. Patton, “Volatility Forecast Comparison Using Imperfect Volatility Proxies,” Journal of Econometrics, Vol. 160, No. 1, 2011, pp. 246-256. http://dx.doi.org/10.1016/j.jeconom.2010.03.034
[14] P. R. Hansen and A. Lunde, “Consistent Ranking of Volatility Models,” Journal of Econometrics, Vol. 131, No. 1, 2006, pp. 97-121. http://dx.doi.org/10.1016/j.jeconom.2005.01.005

  
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