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Risk Analysis for Three Precious Metals: An Application of Extreme Value Theory

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Abstract

Gold, and other precious metals, are among the oldest and most widely held commodities used as a hedge against the risk of disruptions in financial markets. The prices of such metals fluctuate substantially, introducing risks of their own. This paper’s goal is to analyze the risk of investment in gold, silver, and platinum by applying Extreme Value Theory to historical daily data for changes in their prices. The risk measures adopted in this paper are Value at Risk and Expected Shortfall. Estimates of these measures are obtained by fitting the Generalized Pareto Distribution, using the Peaks-Over-Threshold method, to the extreme daily price changes. The robustness of the results to changes in the sample period, threshold choice, and distributional assumptions, are discussed. Our results show that silver is the most risky metal among the three considered. For negative daily returns, platinum is riskier than gold; while the converse is true for positive returns.

Suggested Citation

  • David E. Giles & Qinlu Chen, 2017. "Risk Analysis for Three Precious Metals: An Application of Extreme Value Theory," Econometrics Working Papers 1704, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicewp:1704
    Note: ISSN 1485-6441
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    References listed on IDEAS

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    1. Mahdi Tavangar & Majid Asadi, 2012. "Some unified characterization results on the generalized Pareto distributions based on generalized order statistics," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(7), pages 997-1007, October.
    2. Feng Ren & David E. Giles, 2007. "Extreme Value Analysis of Daily Canadian Crude Oil Prices," Econometrics Working Papers 0708, Department of Economics, University of Victoria.
    3. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    4. Krenar Avdulaj, 2011. "The Extreme Value Theory as a Tool to Measure Market Risk," Working Papers IES 2011/26, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2011.
    5. L. E. Blose & V. Gondhalekar, 2014. "Overnight gold returns," Applied Economics Letters, Taylor & Francis Journals, vol. 21(18), pages 1269-1272, December.
    6. Singh, Abhay K. & Allen, David E. & Robert, Powell J., 2013. "Extreme market risk and extreme value theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 310-328.
    7. Paul Embrechts & Sidney Resnick & Gennady Samorodnitsky, 1999. "Extreme Value Theory as a Risk Management Tool," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 30-41.
    8. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    9. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling commodity value at risk with higher order neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(7), pages 585-600.
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    Cited by:

    1. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.

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    More about this item

    Keywords

    Precious metals; extreme values; portfolio risk; value-at-risk; generalized Pareto distribution;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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