Abstract
This study proposes a new range-based Markov-switching dynamic conditional correlation (MSDCC) model for estimating the minimum-variance hedging ratio and comparing its hedging performance with that of alternative conventional hedging models, including the naive, OLS regression, return-based DCC, range-based DCC and return-based MS-DCC models. The empirical results show that the embedded Markov-switching adjustment in the range-based DCC model can clearly delineate uncertain exogenous shocks and make the estimated correlation process more in line with reality. Overall, in-sample and out-of sample tests indicate that the range-based MS-DCC model outperforms other static and dynamic hedging models.
Highlights
The dynamic conditional correlation (DCC) model, the celebrated multivariate correlation estimation model proposed by Engle [1], solves the requirement of a positive definite constraint in parameter estimation, the ability to estimate many parameters and time-varying correlation1
A new range-based Markov-switching dynamic conditional correlation model is proposed to address minimum variance hedging for futures
The estimated results show that the dynamic correlation process is derived by both the low- and high-correlation states by means of an estimated endogenous transition probability for both stock indices
Summary
The dynamic conditional correlation (DCC) model, the celebrated multivariate correlation estimation model proposed by Engle [1], solves the requirement of a positive definite constraint in parameter estimation, the ability to estimate many parameters and time-varying correlation. We propose a new range-based regime-switching DCC model that is able to enhance the hedge effect in futures markets. Pelletier [17] and Billio and Caporin [18] propose a variant multivariate GARCH model composed of a Markov chain and the DCC model These studies consider a solution that a discrete level shift may exist in the dynamic conditional correlation process. They verify that the return-based Markov-switching DCC model outperforms Engle’s [1] single-regime DCC model structure. This study employs a range-based Markov-switching dynamic conditional correlation (MS-DCC) model to estimate the dynamic hedge ratio and discusses the hedging effectiveness of other approaches.
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