Correspondence: Lorne N. Switzer, John Molson School of Business, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec, Canada H3G 1M8. E-mail: switz@jmsb.concordia.ca

¹is Associate Dean, Research and the Van Berkom Endowed Chair in Small Cap Equities in the Finance Department, John Molson School of Business at Concordia University. He also serves as the Associate Director of the Concordia-HEC Montreal Institute for Governance of Private and Public Organizations. He has published numerous academic articles and books and has served since 1994 as an associate editor for European Financial Management and is on the Scientific Committee of La Revue Financier. He has served as a consultant for many business firms and organisations, including the Caisse de Dépot et Placement du Québec, Schlesinger Newman Goldman Inc., AMI Partners Inc., Bank Credit Analysts Research Group, Institute for Canadian Bankers, and the Bourse de Montréal. He obtained his PhD from the University of Pennsylvania in 1982.

²is a financial services analyst with Montrusco Bolton Investments Inc., Montreal, Canada. Before joining the firm, Andrey was a research associate with Dundee Securities Corporation from 2006 to 2007 with responsibilities for the paper and forest products and steels sectors. From 2004 to 2005, he worked as an analyst for Bellator Fund Management focusing on energy futures. He is a graduate of Concordia University, and holds an MSc in Administration, with a concentration in Finance, and a BCom with a major in Finance and a minor in Economics. Andrey is a certified financial risk manager charterholder.

Received 10 October 2008; Revised 10 October 2008.

Abstract

This paper looks at the risk-adjusted performance of dynamic asset allocation strategies across hedge fund indices using conditional volatility forecasting methods for constructing optimal portfolios for funds of funds. Monthly out-of-sample comparisons for nine Credit Suisse First Boston/Tremont hedge fund indices, as well as weekly and daily rebalanced dynamic portfolios are examined for the three main sub-indices of Standard & Poor's (S&P) Hedge Fund Index. A multivariate asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is also considered for portfolio construction using daily S&P Hedge Fund sub-indices data. Most hedge fund indices exhibit time-varying volatility and volatility clustering. Accounting for forecasted next-period volatility generates portfolios with the best risk-return profile among all portfolios under consideration. After accounting for transaction costs, out-of-sample results indicate that all dynamic hedge fund index portfolios largely outperform the S&P 500 Index, both on an expected return and risk-adjusted return basis.