Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy

DOI: 10.4236/jmf.2012.21014   PDF   HTML     8,333 Downloads   20,239 Views   Citations


In this paper, we forecast the volatility of gold prices using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting gold price volatility. The MRS-GARCH is best performance model for gold price volatility in some loss function. Moreover, we forecast closing prices of gold price to trade future contract. MRS-GARCH got the most cumulative return same GJR model.

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N. Sopipan, P. Sattayatham and B. Premanode, "Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy," Journal of Mathematical Finance, Vol. 2 No. 1, 2012, pp. 121-131. doi: 10.4236/jmf.2012.21014.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. Mehmet, “Analysis of Turkish Financial Market with Markov Regime Switching Volatility Models,” The Middle East Technical University, Ankara 2008.
[2] R. Engle, “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Vol. 50, No. 4, 1982, pp. 987-1008. doi:10.2307/1912773
[3] T. Bollerslev, “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics, Vol. 31, No. 3, 1986, pp. 307-327. doi:10.1016/0304-4076(86)90063-1
[4] D. B. Nelson, “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, Vol. 59, No. 2, 1991, pp. 347-370. doi:10.2307/2938260
[5] L. R. Glosten, R. Jagannathan and D. Runkle, “On the Relation between the Expected Value and the Nominal Excess Return on Financials,” Journal of Finance, Vol. 48, No. 5, 1993, pp. 1779-1801. doi:10.2307/2329067
[6] J. D. Haminton and R. Susmel, “Autoregressive Conditional Heteroskedasticity and Change in Regime,” Journal of Econometrics, Vol. 64, No. 1-2, 1994, pp. 307-333. doi:10.1016/0304-4076(94)90067-1
[7] Z. F. Guo and L. Cao, “A Smooth Transition GARCH Model with Asymmetric Transition Phases,” Proceedings of International Conference of Financial Engineering, London, 6-8 July 2011.
[8] Z. F. Guo and L. Cao, “An Asymmetric Smooth Transition GARCH Model,” IAENG Journals, 2011.
[9] J. Marcucci, “Forecasting Financial Market Volatility with Regime-Switching GARCH Model,” Working Paper, University of California, San Diego, 2005.
[10] T. Edel and M. Brian, “APGARCH Investigation of the Main Influences on the Gold Price,” University of Dublin, Dublin, 2005.
[11] S. Gray, “Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process,” Journal of Financial Economics, Vol. 42, No. 1, 1996, pp. 27-62. doi:10.1016/0304-405X(96)00875-6
[12] F. Klaanssen, “Improving GARCH Volatility Forecasts with Regime-Switching GARCH,” Empirical Economics, Vol. 27, No. 2, 2002, pp. 363-394. doi:10.1007/s001810100100
[13] C. J. Kimand and C. R. Nelson, “State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications,” MIT Press, Cambridge, 1999.
[14] J. D. Hamilton, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, Vol. 57, No. 2, 1989, pp. 357-384. doi:10.2307/1912559
[15] J. D. Hamilton, “Analysis of Time Series Subject to Change in Regime,” Journal of Econometrics, Vol. 45, No. 1-2, 1990, pp. 39-70. doi:10.1016/0304-4076(90)90093-9

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