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

Michiel De Pooter, Martin Martens () and Dick van Dijk
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Martin Martens: Faculty of Economics, Erasmus Universiteit Rotterdam

No 05-089/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: realized volatility; high-frequency data; volatility timing; mean-variance analysis; tracking error (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2005-10-12, Revised 2006-01-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20050089

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