The Bayesian MS-GARCH model and Value-at-Risk in South African agricultural commodity price markets

The core objective of this paper is to examine the relationship between the prices of agricultural commodities with the oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at- Risk (VaR). The data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH, and the Markov-switching GARCH (MS-GARCH) models. To choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under different distributions for innovations. The results indicate that the price of agricultural commodities was found to be significantly associated with the price of coal, the price of natural gas, price of oil and exchange rate. Moreover, for most of the agricultural commodities considered in this paper, the MS-GARCH models under the MCMC approach outperformed the standard single regime GARCH models in measuring VaR. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MSGARCH processes.

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Conference Paper/ Presentation
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JEL Codes:
G17; C05

 Record created 2018-08-17, last modified 2020-10-28

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