JMF  Vol.2 No.1 , February 2012
Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy
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.

Cite this paper
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.
[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