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Dynamic correlation of precious metals and flight-to-quality in developed markets

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  • Klein, Tony

Abstract

A flexible modification of the DCC model that accounts for asymmetry and long memory in variance is proposed. This model is applied on precious metals and indexes of developed countries to revisit the flight-to-quality phenomenon. Market turmoil and shocks are covered by asset-specific variance models. I identify Gold and partly Silver as safe haven while this status seems to be dissipating in the recent years. Interestingly, Platinum shows signs of a surrogate safe haven. The practical difference between the standard DCC and the model proposed herein is significant, which stems from a more realistic variance modeling within the framework.

Suggested Citation

  • Klein, Tony, 2017. "Dynamic correlation of precious metals and flight-to-quality in developed markets," Finance Research Letters, Elsevier, vol. 23(C), pages 283-290.
    • Handle: RePEc:eee:finlet:v:23:y:2017:i:c:p:283-290
    DOI: 10.1016/j.frl.2017.05.002
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    References listed on IDEAS

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    1. 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.
    2. Klein, Tony & Walther, Thomas, 2017. "Fast fractional differencing in modeling long memory of conditional variance for high-frequency data," Finance Research Letters, Elsevier, vol. 22(C), pages 274-279.
    3. Hammoudeh, Shawkat M. & Yuan, Yuan & McAleer, Michael & Thompson, Mark A., 2010. "Precious metals-exchange rate volatility transmissions and hedging strategies," International Review of Economics & Finance, Elsevier, vol. 19(4), pages 633-647, October.
    4. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    5. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    6. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    7. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    8. Sensoy, Ahmet, 2013. "Dynamic relationship between precious metals," Resources Policy, Elsevier, vol. 38(4), pages 504-511.
    9. Massari, Stefania & Ruberti, Marcello, 2013. "Rare earth elements as critical raw materials: Focus on international markets and future strategies," Resources Policy, Elsevier, vol. 38(1), pages 36-43.
    10. Hood, Matthew & Malik, Farooq, 2013. "Is gold the best hedge and a safe haven under changing stock market volatility?," Review of Financial Economics, Elsevier, vol. 22(2), pages 47-52.
    11. 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.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. Chkili, Walid, 2016. "Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, vol. 38(C), pages 22-34.
    14. Dirk Baur & Duy Tran, 2014. "The long-run relationship of gold and silver and the influence of bubbles and financial crises," Empirical Economics, Springer, vol. 47(4), pages 1525-1541, December.
    15. Mensi, Walid & Hammoudeh, Shawkat & Kang, Sang Hoon, 2017. "Dynamic linkages between developed and BRICS stock markets: Portfolio risk analysis," Finance Research Letters, Elsevier, vol. 21(C), pages 26-33.
    16. 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.
    17. Y. K. Tse, 1998. "The conditional heteroscedasticity of the yen-dollar exchange rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 49-55.
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    More about this item

    Keywords

    Dynamic correlations; Precious metals; Stock markets; Asymmetry; Long memory;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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