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On the gains of using high frequency data and higher moments in Portfolio Selection

Author

Listed:
  • Rui Pedro Brito

    (CeBER and Faculty of Economics of the University of Coimbra)

  • Hélder Sebastião

    (CeBER and Faculty of Economics of the University of Coimbra)

  • Pedro Godinho

    (CeBER and Faculty of Economics of the University of Coimbra)

Abstract

In this paper we conduct an empirical analysis on the performance gains of using high frequency data in Portfolio Selection. Within a CRRA-utility maximization framework, we suggest the construction of two different portfolios: a low and a high frequency portfolio. For ten different risk aversion levels, we compare the performance of both portfolios in terms of several out-of-sample measures. Using data on fourteen stocks of the CAC 40 stock market index, from January 1999 to December 2003, we conclude that the “fight” is always “won” by the high frequency portfolio for all the considered performance evaluation measures.

Suggested Citation

  • Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2017. "On the gains of using high frequency data and higher moments in Portfolio Selection," CeBER Working Papers 2017-02, Centre for Business and Economics Research (CeBER), University of Coimbra.
    • Handle: RePEc:gmf:papers:2017-02
    as

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    References listed on IDEAS

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    Cited by:

    1. Seema REHMAN & Saqib SHARIF & Wali ULLAH, 2021. "Higher Realized Moments and Stock Return Predictability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 48-70, December.

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    More about this item

    Keywords

    portfolio selection; utility maximization criteria; higher moments; high frequency data; out-of-sample analysis.;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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