Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets

Abstract

We use the All Ordinaries Index and the corresponding Share Price Index futures contract written against the All Ordinaries Index to estimate optimal hedge ratios, adopting several specifications: an ordinary least squares-based model, a vector autoregression, a vector error-correction model and a diagonal-vec multivariate generalized autoregressive conditional heteroscedasticity model. Hedging effectiveness is measured using a risk-return comparison and a utility maximization method. We find that time-varying generalized autoregressive conditional heteroscedasticity hedge ratios perform better than constant hedge ratios in terms of minimizing risks, but when return effects are also considered, the utility-based measure prefers the ordinary least squares method in the in-sample hedge, whilst both approaches favour the conditional time-varying multivariate generalized autoregressive conditional heteroscedasticity hedge ratio estimates in out-of-sample analyses. Copyright 2005 Accounting and Finance Association of Australia and New Zealand..

Estimation and Performance Evaluation of Optimal Hedge Ratios in the Carbon Market of the European Union Emissions Trading Scheme

“…As the risk aversion level increases from 1 to 3, utility improvement based on OLS decreases. This is in line with the in-sample results in Yang & Allen (2004). By contrast, utility improvement achieved by VECM-GARCH presents a mixture of results when the risk aversion increases gradually.…”

The effectiveness of index futures hedging in emerging markets during the crisis period of 2008-2010: Evidence from South Africa

“…Hedging of equity exposure by determining the optimal hedge ratio can be approached in various ways. Studies by Ederington (1979), Franckle (1980), Figlewski (1985), Vishwanath (1993), Ghosh (1993), Chou et al (1996), Myers (1991), Park and Switzer (1995), Holmes (1996), Laws and Thompson (2005) as well as Yang and Allen (2005) have applied different | 2 econometric hedging methods in order to estimate the optimal hedge ratio. These methods range from the Single Equation Method estimated by Ordinary Least Squares (Ederington (1979), Franckle (1980) and Figlewski (1985)); the Vector Autoregression Method (Vishwanath (1993), Ghosh (1993) and Chou et al (1996)); Vector ErrorCorrection Method (Vishwanath (1993), Ghosh (1993) and Chou et al (1996)); to a class of Multivariate GARCH methods (Myers (1991), Park and Switzer (1995) Wahab (1995)).…”

Hedging Performance and Stock Market Liquidity: Evidence from the Taiwan Futures Market

“…Previous studies find that the use of advanced econometric models to estimate the optimal hedge ratio can improve the hedging performance (e.g. Alizadeh & Nomikos, 2004;Yang & Allen, 2004;Hsu et al, 2008). In addition, Miffre (2004) finds that the hedging performance for the conditional OLS model outperforms the OLS and the GARCH models.…”

“…Hence, recent studies have used GARCH-type models to characterize the behavior of hedge ratios over time. Myers (1991), Baillie & Myers (1991), and Yang & Allen (2004) find that GARCH-type time-varying optimal hedge ratios outperform constant hedge ratios, indicating that a GARCH approach appears ideally suited to estimating time-varying optimal hedge ratios. Hsu et al (2008) suggest that copula-based GARCH models perform more effectively than OLS, constant conditional correlation (CCC) GARCH, and dynamic conditional correlation (DCC) GARCH models.…”

“…Previous studies have found that complex econometric models such as the error correction and generalized autoregressive conditional heteroskedasticity (GARCH) approaches outperform the OLS model (e.g. Yang & Allen, 2004;Hsu et al, 2008). Furthermore, Miffre (2004) reports that the conditional OLS model outperforms the OLS and GARCH models.…”