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On the Gains of Using High Frequency Data in Portfolio Selection

Author

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  • Brito Rui Pedro

    (Centre for Business and Economics Research (CeBER), Grupo de Estudos Monetários e Financeiros (GEMF), Faculty of Economics, University of Coimbra, Portugal)

  • Sebastião Helder

    (Centre for Business and Economics Research (CeBER), Grupo de Estudos Monetários e Financeiros (GEMF), Faculty of Economics, University of Coimbra, Portugal)

  • Godinho Pedro

    (Centre for Business and Economics Research (CeBER), Faculty of Economics, University of Coimbra, Portugal)

Abstract

This paper analyzes empirically the performance gains of using high frequency data in portfolio selection. Assuming Constant Relative Risk Aversion (CRRA) preferences, with different relative risk aversion levels, we compare low and high frequency portfolios within mean-variance, mean-variance-skewness and mean-variance-skewness-kurtosis frameworks. Using data on fourteen stocks of the Euronext Paris, from January 1999 to December 2005, we conclude that the high frequency portfolios outperform the low frequency portfolios for every out-of-sample measure, irrespectively to the relative risk aversion coefficient considered. The empirical results also suggest that for moderate relative risk aversion the best performance is always achieved through the jointly use of the realized variance, skewness and kurtosis. This claim is reinforced when trading costs are taken into account.

Suggested Citation

  • Brito Rui Pedro & Sebastião Helder & Godinho Pedro, 2018. "On the Gains of Using High Frequency Data in Portfolio Selection," Scientific Annals of Economics and Business, Sciendo, vol. 65(4), pages 365-383, December.
    • Handle: RePEc:vrs:aicuec:v:65:y:2018:i:4:p:365-383:n:8
    DOI: 10.2478/saeb-2018-0030
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    1. repec:hal:journl:peer-00815564 is not listed on IDEAS
    2. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    3. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    4. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
    5. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    6. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
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    More about this item

    Keywords

    Portfolio selection; utility maximization criteria; higher moments; high frequency data;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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