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Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market

Author

Listed:
  • Masato Ubukata

    (Department of Economics, Kushiro Public University of Economics)

Abstract

This paper examines effects of realized covariance matrix estimators based on high-frequency data on large-scale minimum-variance equity portfolio optimization. The main results are: (i) the realized covariance matrix estimators yield a lower standard deviation of large-scale portfolio returns than Bayesian shrinkage estimators based on monthly and daily historical returns; (ii) gains to switching to strategies using the realized covariance matrix estimators are higher for an investor with higher relative risk aversion; and (iii) the better portfolio performance of the realized covariance approach implied by ex-post return per unit of risk and switching fees seems to be robust to the level of transaction costs.

Suggested Citation

  • Masato Ubukata, 2010. "Large-scale portfolios using realized covariance matrix: evidence from the Japanese stock market," Economics Bulletin, AccessEcon, vol. 30(4), pages 2906-2919.
    • Handle: RePEc:ebl:ecbull:eb-10-00571
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    References listed on IDEAS

    as
    1. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    2. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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