Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency To Use?
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||high-frequency data, mean-variance analysis, realized volatility, tracking error, volatility timing|
de Pooter, M.D., Martens, M.P.E., & van Dijk, D.J.C.. (2005). Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency To Use? (No. TI 05-089/4). Retrieved from http://hdl.handle.net/1765/6959