Covariance Averaging for Improved Estimation and Portfolio Allocation

Quantf Research Working Paper Series No. WP11/2014

30 Pages Posted: 15 Jul 2013 Last revised: 2 Jun 2014

Dimitrios D. Thomakos

University of Peloponnese - School of Management, Economics and Informatics; University of Bologna - Rimini Center for Economic Analysis (RCEA)

Fotis Papailias

Quantf Research; University of London, King's College London, Department of Management

Date Written: June 1, 2014

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.

Keywords: Averaging, Covariance Estimation, Financial Returns, Multivariate Time Series, Portfolio Allocation, Risk Management, Rolling Window

Suggested Citation

Thomakos, Dimitrios D. and Papailias, Fotis, Covariance Averaging for Improved Estimation and Portfolio Allocation (June 1, 2014). Quantf Research Working Paper Series No. WP11/2014. Available at SSRN: https://ssrn.com/abstract=2293396 or http://dx.doi.org/10.2139/ssrn.2293396

Dimitrios D. Thomakos

University of Peloponnese - School of Management, Economics and Informatics ( email )

Department of Economics
22100 Tripolis
Greece
+30 2710 230139 (Fax)

HOME PAGE: http://es.uop.gr/

University of Bologna - Rimini Center for Economic Analysis (RCEA)

Via Patara, 3
Rimini (RN), RN 47900
Italy

Fotis Papailias (Contact Author)

Quantf Research ( email )

London
United Kingdom

HOME PAGE: http://www.quantf.com

University of London, King's College London, Department of Management ( email )

150 Stamford Street
London, SE1 9NN
United Kingdom

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