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The dynamics of precious metal markets VaR: A GARCHEVT approach

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  • Zhang, Zijing
  • Zhang, Hong-Kun

Abstract

The data analysis of the metal markets has recently attracted a lot of attention, mainly because the prices of precious metal are relatively more volatile than its historical trend. A robust estimate of extreme loss is vital, especially for mining companies to mitigate risk and uncertainty in metal price fluctuations. This paper examines the Value-at-Risk and statistical properties in daily price return of precious metals, which include gold, silver, platinum, and palladium, from January 11, 2000 to September 9, 2016. An advanced two stage approach which combines GARCH-type models with extreme value theory is implemented. In the first stage, the conditional variance is modeled by different rolling univariate GARCH-type models (GARCH, EGARCH and TGARCH) under the GED error assumption in the returns of precious metal markets and compare the same with other well-known models. In the second stage, extreme value approach is applied to capture the tail behavior of distribution for the extracted standardized residuals. In comparison with the dynamic VaRs of these precious metals, we find that gold has the most steady and the highest VaRs, followed by platinum and silver; on the other hand our results show that palladium has the most volatile VaRs. The backtesting result confirms that our approach is an adequate method in improving risk management assessments and hedging strategies in the high volatile metal markets.

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  • Zhang, Zijing & Zhang, Hong-Kun, 2016. "The dynamics of precious metal markets VaR: A GARCHEVT approach," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 14-27.
    • Handle: RePEc:eee:jocoma:v:4:y:2016:i:1:p:14-27
    DOI: 10.1016/j.jcomm.2016.10.001
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    1. David Hillier & Paul Draper & Robert Faff, 2006. "Do Precious Metals Shine? An Investment Perspective," Financial Analysts Journal, Taylor & Francis Journals, vol. 62(2), pages 98-106, March.
    2. Hammoudeh, Shawkat & Malik, Farooq & McAleer, Michael, 2011. "Risk management of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(4), pages 435-441.
    3. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    4. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    5. 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.
    6. 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.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Hammoudeh, Shawkat & Yuan, Yuan, 2008. "Metal volatility in presence of oil and interest rate shocks," Energy Economics, Elsevier, vol. 30(2), pages 606-620, March.
    9. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    10. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    11. 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.).
    12. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    13. Philippe Jorion, 1996. "Risk and Turnover in the Foreign Exchange Market," NBER Chapters, in: The Microstructure of Foreign Exchange Markets, pages 19-40, National Bureau of Economic Research, Inc.
    14. Bernard Goodman, 1956. "The Price Of Gold And International Liquidity," Journal of Finance, American Finance Association, vol. 11(1), pages 15-28, March.
    15. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    16. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    17. Kittiya Chaithep & Songsak Sriboonchitta & Chukiat Chaiboonsri & Pathairat Pastpipatkul, 2012. "Value at Risk Analysis of Gold Price Returns Using Extreme Value Theory," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 151-168, December.
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    4. Bhatia, Vaneet & Das, Debojyoti & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 55(C), pages 244-252.
    5. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
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    7. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.

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