Hedging agriculture commodities futures with histogram data: a Markov switching volatility and correlation model
by Woraphon Yamaka; Pichayakone Rakpho; Paravee Maneejuk
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 13, No. 3, 2021

Abstract: In this study, the bivariate flexible Markov switching dynamic copula GARCH model is developed to histogram-value data for calculating optimal portfolio weight and optimal hedge. This model is an extension of the Markov switching dynamic copula GARCH in which all estimated parameters are allowed to be a regime dependent. The histogram data is constructed from the five-minute wheat spot and futures returns. We compare our proposed model with other bivariate GARCH models through AIC, BIC, and hedge effectiveness. The empirical results show that our model is slightly better than the conventional methods in terms of the lowest AIC and BIC, and the highest hedge effectiveness. This indicates that our proposed model is quite effective in reducing risks in portfolio returns.

Online publication date: Fri, 08-Oct-2021

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