A copula‐based regime‐switching GARCH model for optimal futures hedging

Abstract

The article develops a regime-switching Gumbel-Clayton (RSGC) copula GARCH model for optimal futures hedging. There are three major contributions of RSGC. First, the dependence of spot and futures return series in RSGC is modeled using switching copula instead of assuming bivariate normality. Second, RSGC adopts an independent switching Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process to avoid the path-dependency problem. Third, based on the assumption of independent switching, a formula is derived for calculating the minimum variance hedge ratio. Empirical investigation in agricultural commodity markets reveals that RSGC provides good out-of-sample hedging effectiveness, illustrating importance of modeling regime shift and asymmetric dependence for futures hedging.

Hedging systematic risk in the commodity market with a regime‐switching multivariate rotated generalized autoregressive conditional heteroskedasticity model

“…The implication is that to estimate the MVHR, the regime‐switching property of the joint distribution of spot and futures returns must be considered. A number of multivariate regime‐switching models have been applied to estimate the regime‐switching time‐varying MVHR (Alizadeh & Nomikos, ; Alizadeh, Huang, & van Dellen, ; Alizadeh, Nomikos, & Pouliasis, ; Dark, ; Lai, Sheu, & Lee, ; Lee, ; ; ; Lee & Yoder, , ; Lien, ; Pan et al, ; Sheu & Lee, ; Yan & Li, ). Most of these studies apply a variety of regime‐switching GARCH models to capture the state‐dependent time‐varying covariance dynamics of spot and futures returns.…”

Regime switching correlation hedging

“…where each conditional variance dynamic is assumed to follow an independent switching GARCH(1,1) process suggested by Hass et al (2004) and Lee (2009b): h j;t ðs t Þ ¼ c j ðs t Þ þ a j ðs t Þe i;tÀ1 þ b j ðs t Þh j;tÀ1 ðs t Þh j;tÀ1 ðs t Þ;…”

“…To further incorporate the effects of unanticipated news events in determining of optimal hedge ratio, Lee (2009a) develops a Markov regime switching generalized orthogonal GARCH model with conditional jump dynamics for estimating the optimal hedge ratio. Further extension is to release the assumption of joint normality between spot and futures returns and use regime switching copula GARCH model for futures hedging (Lee, 2009b). All these elaborations improve futures hedging effectiveness.…”

Optimal Futures Hedging Under Multichain Markov Regime Switching

“…There are three existing single‐state‐variable Markov regime switching hedging models that explicitly specify the state‐dependent time‐varying correlation dynamic. These are the Markov RSVC GARCH (Lee & Yoder, ), the FSDCC GARCH (Lee, ) and the Markov RSCor GARCH (Christodoulakis & Satchell, ; Lee, ). To justify the usefulness of MCSG for dynamic futures hedging, we further compare the hedging effectiveness of MCSG with these models…”

“…MCSG is an extension of MCMS such that volatility dynamics in MCSG follow a regime‐switching GARCH process. In this study, we compare the hedging effectiveness of MCSG with a number of single‐state‐variable regime‐switching GARCH models, including the regime‐switching constant correlation ( RSCC ) GARCH (Pelletier, ) and three state‐dependent time‐varying correlation GARCH models: the Markov regime‐switching varying‐correlation ( RSVC ) GARCH (Lee & Yoder, ), the full‐switching dynamic conditional correlation ( FSDCC ) GARCH (Lee, ) and the Markov regime switching correlated ( RSCor ) GARCH (Christodoulakis & Satchell, ; Lee, ).…”