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Fat Tails, Value at Risk, and the Palladium Returns

Author

Listed:
  • Jianhua Ding

    (Jianghan University, China)

  • Turen Guo

    (Xiangnan College, China)

  • Bin Guo

    (University of Cologne, Germany)

Abstract

The past decade has witnessed the rapid growing of the world palladium market. Thus, it is even more important to develop effective quantitative tools for risk management of palladium assets at this moment. In this paper, the authors investigated five different types of widely-used statistical distributions and employ the industry standard risk measurement, value at risk (VaR), for risk management of daily palladium spot returns. First the four different criteria were applied to compare the goodness of fit of the five distributions, and then calculate the VaRs based on the parameters estimated from the first step. The results indicate the skewed t distribution has the best in-sample fitting and generate VaR values closest to the nonparametric historical VaR values.

Suggested Citation

  • Jianhua Ding & Turen Guo & Bin Guo, 2018. "Fat Tails, Value at Risk, and the Palladium Returns," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 7(2), pages 95-103, May.
    • Handle: RePEc:ods:journl:v:7:y:2018:i:2:p:95-103
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    References listed on IDEAS

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    1. 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.
    2. Mallika Kumar & M. M. Sulphey, 2015. "Investment Option in Gold – A Study on Price Discovery of Gold Futures in India," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 4(4), pages 231-238, November.
    3. Guglielmo Maria Caporale & Fabio Spagnolo & Nicola Spagnolo, 2017. "Macro News and Commodity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 68-80, January.
    4. Mihir Dash & Suprabha Bakshi & Aarushi Chugh, 2017. "The Relationship Between Work Experience and Employee Compensation: A Case Study of the Indian IT Industry," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 6(1), pages 5-10, February.
    5. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.
    6. Michele Bisceglia & Paola Zola, 2018. "Dollar-Cost Averaging with Yearly and Biyearly Installments," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 7(1), pages 1-14, February.
    7. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    8. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    9. Zi-Yi Guo, 2017. "Heavy-tailed Distributions and Risk Management of Equity Market Tail Events," Journal of Risk & Control, Risk Market Journals, vol. 4(1), pages 31-41.
    10. Mihir Dash & Sadguna Kantheti & Guttula Krishna Teja, 2018. "The Book-to-Market Anomaly for Banking Stocks in the Indian Stock Market: A Panel Regression Analysis," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 7(1), pages 15-23, February.
    11. Benjamin R. Auer, 2015. "Superstitious seasonality in precious metals markets? Evidence from GARCH models with time-varying skewness and kurtosis," Applied Economics, Taylor & Francis Journals, vol. 47(27), pages 2844-2859, June.
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    More about this item

    Keywords

    skewed t distribution; goodness of fit; risk management; precious metal;
    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)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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