Long Memory Conditional Volatility and Assett Allocation

44 Pages Posted: 26 May 2011

See all articles by Richard D. F. Harris

Richard D. F. Harris

University of Bristol, School of Accounting and Finance; University of Bristol, School of Accounting and Finance

Anh Nguyen

affiliation not provided to SSRN

Date Written: May 17, 2011

Abstract

In this paper, we evaluate the economic benefits that arise from allowing for long memory in forecasting the covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006). In particular, we compare the statistical and economic performance of four multivariate long memory volatility models (the long memory EWMA, long memory EWMA-DCC, FIGARCH-DCC and component GARCH-DCC models) with that of two short memory models (the short memory EWMA and GARCH-DCC models). We report two main findings. First, for longer horizon forecasts, long memory models produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory models. Second, the two parsimonious long memory EWMA models outperform the other models – both short memory and long memory – at all forecast horizons. These results apply to both low and high dimensional covariance matrices and both low and high correlation assets, and are robust to the choice of estimation window.

Keywords: Conditional variance-covariance matrix, long memory, asset allocation

Suggested Citation

Harris, Richard D. F. and Nguyen, Anh, Long Memory Conditional Volatility and Assett Allocation (May 17, 2011). Available at SSRN: https://ssrn.com/abstract=1844204 or http://dx.doi.org/10.2139/ssrn.1844204

University of Bristol, School of Accounting and Finance

United Kingdom

HOME PAGE: http://www.bristol.ac.uk/people/person/Richard-Harris-50ffa5fb-0e86-4458-8e8c-8dace6eb3435/

Anh Nguyen

affiliation not provided to SSRN ( email )

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