| Summary: | This paper examines hedging in Hong Kong stock index futures. It focuses on different econometric models to estimate constant and time-varying hedge ratios. For both nearby contract and four-month contract of Hang Seng index futures, the various econometric models are used to derive and estimate underlying hedge ratios. OLS regressions, vector autoregressive (VAR) models, vector error correction models (VECM), and multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models are employed to estimated corresponding hedge ratios which is used in hedging for risk management. For the nearby contract, the OLS regressions estimate the optimal hedge ratios in terms of variance reduction (largest risk reduction). For four-month contract, the OLS hedge ratios still perform best in variance reduction in in-sample, but out-of-sample forecast means that the VECM is the best model in hedging for reducing price change risk.
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