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Improved two-component tests in Beta-Skew-t-EGARCH models

Author

Listed:
  • Fernanda Maria Müller

    (Federal University of Rio Grande do Sul)

  • Fábio M Bayer

    (Federal University of Santa Maria)

Abstract

This work proposes a likelihood ratio test to assist in the selection of the Beta-Skew-t-EGARCH model with one or two volatility components. To improve the performance of the proposed test in small samples, the bootstrap-based likelihood ratio test and the bootstrap Bartlett correction are considered. The finite sample performance of the tests are assessed using Monte Carlo simulations. The numerical evidence favors the bootstrap-based test. The tests are applied to the DAX log-returns. The results demonstrate the practical usefulness of the proposed two-component tests.

Suggested Citation

  • Fernanda Maria Müller & Fábio M Bayer, 2017. "Improved two-component tests in Beta-Skew-t-EGARCH models," Economics Bulletin, AccessEcon, vol. 37(4), pages 2364-2373.
    • Handle: RePEc:ebl:ecbull:eb-17-00319
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    References listed on IDEAS

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    1. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    2. Giannetti, Antoine & Clark, John M. & Anderson, Randy I., 2004. "Model risk and option hedging," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(5), pages 659-677, December.
    3. Kascha, Christian & Trenkler, Carsten, 2011. "Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1008-1017, February.
    4. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, November.
    5. Omtzigt Pieter & Fachin Stefano, 2002. "Bootstrapping and Bartlett corrections in the cointegrated VAR model," Economics and Quantitative Methods qf0212, Department of Economics, University of Insubria.
    6. A. Canepa & L. G. Godfrey, 2007. "Improvement of the quasi‐likelihood ratio test in ARMA models: some results for bootstrap methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 434-453, May.
    7. Hull, John & Suo, Wulin, 2002. "A Methodology for Assessing Model Risk and its Application to the Implied Volatility Function Model," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(2), pages 297-318, June.
    8. Canepa, Alessandra, 2016. "A note on Bartlett correction factor for tests on cointegrating relations," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 296-304.
    9. Luger, Richard, 2012. "Finite-sample bootstrap inference in GARCH models with heavy-tailed innovations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3198-3211.
    10. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    11. Stein, Markus Chagas & da Silva, Michel Ferreira & Duczmal, Luiz Henrique, 2014. "Alternatives to the usual likelihood ratio test in mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 184-197.
    12. 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:

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    2. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    3. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers 07-19, Association Française de Cliométrie (AFC).

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    More about this item

    Keywords

    Beta-Skew-t-EGARCH; bootstrap-based test; bootstrap Bartlett correction; likelihood ratio test; two-component test; volatility.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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