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Efficient and robust estimation for financial returns: an approach based on q-entropy

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  • Davide Ferrari
  • Sandra Paterlini

Abstract

We consider a new robust parametric estimation procedure, which minimizes an empirical version of the Havrda-Charv_at-Tsallis entropy. The resulting estimator adapts according to the discrepancy between the data and the assumed model by tuning a single constant q, which controls the trade-o_ between robustness and e_ciency. The method is applied to expected re- turn and volatility estimation of _nancial asset returns under multivariate normality. Theoretical properties, ease of implementability and empirical re- sults on simulated and _nancial data make it a valid alternative to classic robust estimators and semi-parametric minimum divergence methods based on kernel smoothing

Suggested Citation

  • Davide Ferrari & Sandra Paterlini, 2010. "Efficient and robust estimation for financial returns: an approach based on q-entropy," Department of Economics 0623, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    • Handle: RePEc:mod:depeco:0623
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    Cited by:

    1. Yeşim Güney & Y. Tuaç & Ş. Özdemir & O. Arslan, 2021. "Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 47-74, January.
    2. Giuzio, Margherita & Ferrari, Davide & Paterlini, Sandra, 2016. "Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 251-261.

    More about this item

    Keywords

    q-entropy; robust estimation; power-divergence; _nancial returns;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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