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Covariance Averaging for Improved Estimation and Portfolio Allocation

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  • Dimitrios D. Thomakos

    ()
    (Department of Economics, University of Peloponnese, Greece; Quantf Research, Greece; Rimini Centre for Economic Analysis, Italy)

  • Fotis Papailias

    ()
    (Queen's University Management School, Queen's University Belfast, UK; Quantf Research, Greece)

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    Abstract

    We propose a new method for estimating the covariance matrix of a multivariate time series of nancial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the nal covariance estimate. We extend the idea of (model) covariance averaging o ered in the covariance shrinkage approach by means of greater ease of use, exibility and robustness in averaging information over different data segments. The suggested approach does not su er from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.

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    Bibliographic Info

    Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 66_13.

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    Date of creation: Dec 2013
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    Handle: RePEc:rim:rimwps:66_13

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    Related research

    Keywords: averaging; covariance estimation; nancial returns; multivariate time series; portfolio allocation; risk management; rolling window;

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    1. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    2. Kourtis, Apostolos & Dotsis, George & Markellos, Raphael N., 2012. "Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2522-2531.
    3. Olivier Ledoit & Pedro Santa-Clara & Michael Wolf, 2003. "Flexible Multivariate GARCH Modeling with an Application to International Stock Markets," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 735-747, August.
    4. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
    5. Fan, Jianqing & Fan, Yingying & Lv, Jinchi, 2008. "High dimensional covariance matrix estimation using a factor model," Journal of Econometrics, Elsevier, vol. 147(1), pages 186-197, November.
    6. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    7. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH model," Working Paper Series in Economics and Finance 0652, Stockholm School of Economics.
    8. Todd E. Clark & Michael W. McCracken, 2008. "Improving forecast accuracy by combining recursive and rolling forecasts," Working Papers 2008-028, Federal Reserve Bank of St. Louis.
    9. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
    10. Inoue, Atsushi & Rossi, Barbara, 2011. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers.
    11. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," Economics Series 2011_1, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
    12. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(03), pages 621-656, September.
    13. Huo, Lijuan & Kim, Tae-Hwan & Kim, Yunmi, 2012. "Robust estimation of covariance and its application to portfolio optimization," Finance Research Letters, Elsevier, vol. 9(3), pages 121-134.
    14. Bajeux-Besnainou, Isabelle & Bandara, Wachindra & Bura, Efstathia, 2012. "A Krylov subspace approach to large portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 36(11), pages 1688-1699.
    15. Kwan, Clarence C.Y., 2008. "Estimation error in the average correlation of security returns and shrinkage estimation of covariance and correlation matrices," Finance Research Letters, Elsevier, vol. 5(4), pages 236-244, December.
    16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
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