Evaluating the Hedging Performance of the Constant- Correlation GARCH Model

This paper compares the performances of the hedge ratios estimated from the OLS (ordinary least squares) method and the constant-correlation VGARCH (vector generalized autoregressive conditional heteroscedasticity) model. These methods are evaluated based on the out-of-sample optimal hedge ratio forecasts. A systematic comparison is provided by examining ten spot and futures markets covering currency futures, commodity futures and stock index futures. Using a recently proposed test (Tse (2000)) for the constant-correlation assumption, we find that the assumption cannot be rejected for eight of the ten series. To gain the maximum benefit of a time-varying hedging strategy we keep the estimation data up-to-date for the re-estimation of the hedge ratios. Both the constant hedge ratio (using OLS) and the time-varying hedge ratio (using constant-correlation VGARCH) are re-estimated on a day-by-day rollover, and the postsample variances of the hedged portfolios are examined. We find that the OLS hedge ratio performs better than the VGARCH hedge ratio. This result may be another indication that the forecasts generated by the VGARCH models are too variable. Donald Lien is a Professor of Economics and Finance at the University of Kansas. Y. K. Tse and Albert K. C. Tsui are, respectively, Professor and Associate Professor of the Department of Economics, National University of Singapore. An earlier version of the paper was presented at the Tenth Annual Asia-Pacific Futures Research Symposium of the Chicago Board of Trade, February 1999. Comments from the participants of the Symposium and the discussant, Paul Kofman, are gratefully acknowledged. Tse’s research was supported by the National University of Singapore Academic Research Grant RP3981003.