Optimal Weight For Realized Variance Based On Intermittent High-Frequency Data

Hiroki Masuda, Takayuki Morimoto

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    Japanese stock markets have two types of breaks, overnight and lunch, during which no trading occurs, causing an inevitable increased variance in estimating daily volatility via a naive realized variance (RV). In order to perform a more stabilized estimation, we modify Hansen and Lunde's weighting technique. As an empirical study, we estimate optimal weights by using a particular approach for Japanese stock data listed on the Tokyo Stock Exchange, and then compare the forecast performance of weighted and non-weighted RV through an autoregressive fractionally integrated moving average model. The empirical result indicates that the appropriate use of the optimally weighted RV can lead to remarkably smaller estimation variance compared with the naive RV, in many series. Therefore a more accurate forecasting of daily volatility data is obtained. Finally, we perform a Monte Carlo simulation to support the empirical result.

    Original languageEnglish
    Pages (from-to)497-527
    Number of pages31
    JournalJapanese Economic Review
    Issue number4
    Publication statusPublished - Dec 2012

    All Science Journal Classification (ASJC) codes

    • Economics and Econometrics


    Dive into the research topics of 'Optimal Weight For Realized Variance Based On Intermittent High-Frequency Data'. Together they form a unique fingerprint.

    Cite this