Articlegreen2008

Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?

Michiel D. De Pooter · Martin P.E. Martens · Dick J.C. van Dijk

139 citations0/0 files storedEconometric Reviews

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

Abstract

This article investigates the merits of high-frequency intraday data when forming mean-variance efficient stock 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 as judged by the performance of these portfolios. The optimal sampling frequency ranges between 30 and 65 minutes, considerably lower than the popular five-minute frequency, which typically is motivated by the aim of striking a balance between the variance and bias in covariance matrix estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. Bias-correction procedures, based on combining low-frequency and high-frequency covariance matrix estimates and on the addition of leads and lags do not substantially affect the optimal sampling frequency or the portfolio performance. Our findings are also robust to the presence of transaction costs and to the portfolio rebalancing frequency.

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References

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Cited by

2018Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions 2009On portfolio optimization: How and when do we benefit from high‐frequency data?2008Using High-Frequency Data in Dynamic Portfolio Choice 2013Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?2008Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns 2008Realized volatility forecasting and option pricing

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