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Predicting the Daily Covariance Matrix for S&P 100 Stocks using Intraday Data - But which Frequency to use?

Author

Listed:
  • Michiel de Pooter

    (Faculty of Economics, Erasmus Universiteit Rotterdam)

  • Martin Martens

    (Faculty of Economics, Erasmus Universiteit Rotterdam)

  • Dick van Dijk

    (Faculty of Economics, Erasmus Universiteit Rotterdam)

Abstract

This discussion paper resulted in a publication in 'Econometric Reviews', 2008, 27, 199-229. This paper investigates the merits of high-frequency intraday data when forming minimum variance portfolios and minimum tracking error portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency, which strikes a balance between variance and bias in covariance matrix estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. The optimal sampling frequency typically ranges between 30- and 65-minutes, considerably lower than the popular five-minute frequency. We also examine how bias-correction procedures, based on the addition of leads and lags and on scaling, and a variance-reduction technique, based on subsampling, affect the performance.

Suggested Citation

  • Michiel de Pooter & Martin Martens & Dick van Dijk, 2005. "Predicting the Daily Covariance Matrix for S&P 100 Stocks using Intraday Data - But which Frequency to use?," Tinbergen Institute Discussion Papers 05-089/4, Tinbergen Institute, revised 03 Jan 2006.
    • Handle: RePEc:tin:wpaper:20050089
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    References listed on IDEAS

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    More about this item

    Keywords

    realized volatility; high-frequency data; volatility timing; mean-variance analysis; tracking error;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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    This paper has been announced in the following NEP Reports:

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