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Covariance averaging for improved estimation and portfolio allocation

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Abstract

We propose a new method for estimating the covariance matrix of a multivariate time series of financial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the final covariance estimate. We extend the idea of (model) covariance averaging offered in the covariance shrinkage approach by means of greater ease of use, flexibility and robustness in averaging information over different data segments. The suggested approach does not suffer from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization. Copyright Swiss Society for Financial Market Research 2015

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  • Fotis Papailias & Dimitrios Thomakos, 2015. "Covariance averaging for improved estimation and portfolio allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(1), pages 31-59, February.
  • Handle: RePEc:kap:fmktpm:v:29:y:2015:i:1:p:31-59
    DOI: 10.1007/s11408-014-0242-0
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    More about this item

    Keywords

    Averaging; Covariance estimation; Portfolio allocation; Rolling window; C32; C58; G11;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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