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Asymmetries in the Volatility of Precious Metals Returns: The TA-ARSV Modelling Strategy

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  • García-Centeno, María del Carmen
  • Fernández-Avilés, Gema
  • Montero, José María

Abstract

This article shows different models that are capable of reproducing the stylized facts of financial returns series, and provides a new strategy to model the asymmetric answer of volatility in high-frequency series: the TA-ARSV strategy. This strategy is based on the TGARCH and ARSV models. The database used includes the daily returns of gold, silver, and platinum because these metals are currently (at crisis time) considered as an alternative to reserve currencies. Our analysis focuses on the period January 1, 1990 to February 25, 2009. Results show that the TA-ARSV model is the best in presence of leverage effect.

Suggested Citation

  • García-Centeno, María del Carmen & Fernández-Avilés, Gema & Montero, José María, 2010. "Asymmetries in the Volatility of Precious Metals Returns: The TA-ARSV Modelling Strategy," The Journal of Economic Asymmetries, Elsevier, vol. 7(1), pages 23-41.
    • Handle: RePEc:eee:joecas:v:7:y:2010:i:1:p:23-41
    DOI: 10.1016/j.jeca.2010.01.003
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    References listed on IDEAS

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    1. Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998. "Statistical Algorithms for Models in State Space Using SsfPack 2.2," Other publications TiSEM 8fe36759-6517-4c66-86fa-e, Tilburg University, School of Economics and Management.
    2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    3. Siem Jan Koopman & Eugenie Hol Uspensky, 2002. "The stochastic volatility in mean model: empirical evidence from international stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(6), pages 667-689.
    4. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
    5. Timo Terasvirta & Zhenfang Zhao, 2011. "Stylized facts of return series, robust estimates and three popular models of volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 21(1-2), pages 67-94.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    2. Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020. "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).

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    More about this item

    Keywords

    C22; C51; Asymmetric stochastic volatility; TA-ARSV model; Conditional heteroskedasticity; Stylized fact;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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