Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

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.

has been cited by the following article:

  • TITLE: Forecasting Realized Volatility Using Subsample Averaging

    AUTHORS: Huiyu Huang, Tae-Hwy Lee

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

    JOURNAL NAME: Open Journal of Statistics, Vol.3 No.5, October 9, 2013

    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.