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

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  • Shiferaw, Y.

Abstract

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.

Suggested Citation

  • Shiferaw, Y., 2018. "The Bayesian MS-GARCH model and Value-at-Risk in South African agricultural commodity price markets," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275991, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae18:275991
    DOI: 10.22004/ag.econ.275991
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    1. Rodrik, Dani, 1999. "Where Did All the Growth Go? External Shocks, Social Conflict, and Growth Collapses," Journal of Economic Growth, Springer, vol. 4(4), pages 385-412, December.
    2. Acemoglu, Daron & Johnson, Simon & Robinson, James & Thaicharoen, Yunyong, 2003. "Institutional causes, macroeconomic symptoms: volatility, crises and growth," Journal of Monetary Economics, Elsevier, vol. 50(1), pages 49-123, January.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    5. Tony Addison & Atanu Ghoshray & Michalis P. Stamatogiannis, 2016. "Agricultural Commodity Price Shocks and Their Effect on Growth in Sub-Saharan Africa," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(1), pages 47-61, February.
    6. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    7. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
    8. Raymond, Jennie E & Rich, Robert W, 1997. "Oil and the Macroeconomy: A Markov State-Switching Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(2), pages 193-213, May.
    9. von Arnim, Rudi & Tröster, Bernhard & Staritz, Cornelia & Raza, Werner, 2015. "Commodity dependence and price volatility in least developed countries: A structuralist computable general equilibrium model with applications to Burkina Faso, Ethiopia and Mozambique," Working Papers 52, Austrian Foundation for Development Research (ÖFSE).
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Blattman, Christopher & Hwang, Jason & Williamson, Jeffrey G., 2007. "Winners and losers in the commodity lottery: The impact of terms of trade growth and volatility in the Periphery 1870-1939," Journal of Development Economics, Elsevier, vol. 82(1), pages 156-179, January.
    12. Raymond, Jennie E & Rich, Robert W, 1997. "Erratum [Oil and the Macroeconomy: A Markov State-Switching Approach]," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(4), pages 555-555, November.
    13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    14. Berg, Andreas & Meyer, Renate & Yu, Jun, 2004. "Deviance Information Criterion for Comparing Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 107-120, January.
    15. Antonaci, Lavinia & Demeke, Mulat & Vezzani, Antonio, 2014. "The challenges of managing agricultural price and production risks in sub-Saharan Africa," ESA Working Papers 288979, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    16. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    17. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    18. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    19. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    20. Guglielmo Maria Caporale & Nikitas Pittis & Nicola Spagnolo, 2003. "IGARCH models and structural breaks," Applied Economics Letters, Taylor & Francis Journals, vol. 10(12), pages 765-768.
    21. Marc F. Bellemare & Christopher B. Barrett & David R. Just, 2013. "The Welfare Impacts of Commodity Price Volatility: Evidence from Rural Ethiopia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(4), pages 877-899.
    22. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

    Agricultural and Food Policy; International Development;

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