Title: Hedging agriculture commodities futures with histogram data: a Markov switching volatility and correlation model

Authors: Woraphon Yamaka; Pichayakone Rakpho; Paravee Maneejuk

Addresses: Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand ' Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand ' Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand

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.

Keywords: hedging strategy; Markov switching; time-varying dependence; histogram data; wheat.

DOI: 10.1504/IJDMMM.2021.118026

International Journal of Data Mining, Modelling and Management, 2021 Vol.13 No.3, pp.299 - 315

Received: 09 Sep 2019
Accepted: 06 Apr 2020
Published online: 08 Oct 2021 *

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