Modelling and Forecasting Volatility in the Gold Market

Authors

  • Stefan Trück Macquarie University, Australia
  • Kevin Liang Macquarie University, Australia

DOI:

https://doi.org/10.32890/ijbf2012.9.1.3

Keywords:

Gold Markets, Volatility, Forecasting, Value-at-Risk, Backtesting

Abstract

We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast daily volatility and VaR. Both in-sample and out-of-sample forecasts are evaluated using appropriate evaluation measures. For in-sample forecasting, the class of TARCH models provide the best results. For out-of-sample forecasting, the results were not that clear-cut and the order and specification of the models were found to be an important factor in determining model’s performance. VaR for traders with long and short positions were evaluated by comparing failure rates and a simple AR as well as a TARCH model perform best for the considered back-testing period. Overall, most models outperform a benchmark random walk model, while none of the considered models perform significantly better than the rest with respect to all adopted criteria.

 

References

Akgiray, V. (1989). Conditional heteroskedasticity in time series of stock returns: evidence and forecasts. The Journal of Business, 62(1): 55-80.

Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3): 203-228.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3) : 307-327.

Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of Econometrics, 52(1-2) : 5-59.

Brooks, C. (1998). Predicting stock index volatility: can market volume help? Journal of Forecasting, 17(1) : 59-80.

Brooks, C., & Persand, G. (2002). Models choice and value at risk performance. Financial Analysts Journal, 58(5) : 87-97.

Brooks, C., & Persand, G. (2003). Volatility forecasting for risk management. Journal of Forecasting, 22(1) : 1-22.

Canarella, G., & Pollard, S. K. (2008). Modelling the volatility of the London gold market fixing as an asymmetric power ARCH. The Journal of Applied Finance, 14(5) : 17-43.

Chou, R. Y. (1988). Volatility persistence and stock valuation: some empirical evidence using GARCH. Journal of Applied Econometrics, 3(4) : 279-294.

Christoffersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 39(4) : 841-862. Trück and Liang: Forecasting volatility in the gold market

Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk management. Review of Economics and Statistics, 82(1) : 12-22.

Danielsson, J., & de Vries, C. G. (2000). Value-at-risk and extreme returns. Annales d'Economie et de Statistique(60) : 11.

Diebold, F. X. (1998). Elements of Forecasting. Cincinnati, Ohio: South Western College Publishing.

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3) : 253-263.

Dimson, E., & Marsh, P. (1990). Volatility forecasting without data-snooping. Journal of Banking and Finance, 14(2-3) : 399-421.

Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1) : 83-106.

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4) : 987-1007.

Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Eonometrica, 55(2) : 391-407.

Franses, P. H., & van Dijk, D. (1996). Forecasting stock market volatility using (non-linear) GARCH models. Journal of Forecasting, 15: 229-235.

French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1) : 3-29.

Giot, P., & Laurent, S. (2003). Market risk in commodity markets: a VaR approach. Energy Economics, 25(5) : 435-457.

Giot, P., & Laurent, S. (2003). Value-at-risk for long and short trading positions. Journal of Applied Econometrics, 18(6) : 641-663.

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the normal excess return on stocks. The Journal of Finance, 48(5) : 1779-1801.

Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1,1). Journal of applied econometrics, 20(7) : 873-889.

Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills. Journal of Business, 54(4) : 513-533.

Hentschel, L. (1995). All in the family, nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, 39(1) : 71-104.

Hull, J. C. (2007). Risk management and financial institutions: Pearson Prentice Hall.

Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoskedasticity and serial independence of regression residuals. Economics Letters, 6(3) : 255-259.

Jorion, P. (1996). Risk2: Measuring the risk in value at risk. Financial Analysts Journal, 52(6) : 47-56.

Jun, J. H. (2009) Global financial crisis and gold market. Working Paper.

Kupiec, P. (1995). Techniques for veryfying the accuracy of risk management models. Journal of Derivatives, 3(2) : 73-84.

McMillan, D., Speight, A., & Apgwilym, O. (2000). Forecasting UK stock market volatility. Applied Financial Economics, 10(4) : 435-448.

Morales, L. (2008). Volatility spillovers on precious metals markets: the effects of the Asian crisis. Paper presented at the European Applied Business Research Conference (EABR), Salzburg, Austria.

J.P. Morgan., RiskMetrics Monitor, Fourth Quarter, 1995.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59(2) : 347-370.

Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28(4) : 467-488.

Stock, J. H., & Watson, M. W. (1998). A comparison of linear and non-linear univariate models for forecasting macroeconomic time series. NBER Working Paper, 6607.

Tully, E., & Lucey, B. M. (2007). A power GARCH examination of the gold market. Research in International Business and Finance, 21(2) : 316-325.

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Published

22-03-2012

Issue

Vol. 9 No. 1 (2012): International Journal of Banking and Finance (IJBF) Vol. 9, No.1, 2012

Section

Articles

How to Cite

Trück, S., & Liang, K. (2012). Modelling and Forecasting Volatility in the Gold Market. International Journal of Banking and Finance, 9(1), 48-80. https://doi.org/10.32890/ijbf2012.9.1.3

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Harvested 2026-09-07
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Identifiers DOI 10.32890/ijbf2012.9.1.3 OpenAlex W7049016498