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A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)


  • José Dias Curto


  • João Tomaz
  • José Castro Pinto


No abstract is available for this item.

Suggested Citation

  • José Dias Curto & João Tomaz & José Castro Pinto, 2009. "A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 8(1), pages 23-36, April.
  • Handle: RePEc:spr:portec:v:8:y:2009:i:1:p:23-36 DOI: 10.1007/s10258-009-0037-9

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    References listed on IDEAS

    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Liu, Shi-Miin & Brorsen, B Wade, 1995. "Maximum Likelihood Estimation of a Garch-Stable Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(3), pages 273-285, July-Sept.
    3. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Niklas Wagner & Terry A. Marsh, 2004. "Surprise Volume and Heteroskedasticity in Equity Market Returns," Econometrics 0409009, EconWPA.
    6. David Harvey & Paul Newbold, 2000. "Tests for multiple forecast encompassing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 471-482.
    7. Niklas Wagner & Terry Marsh, 2005. "Surprise volume and heteroskedasticity in equity market returns," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 153-168.
    8. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    9. Stefan Mittnik & Marc Paolella & Svetlozar Rachev, 1998. "Unconditional and Conditional Distributional Models for the Nikkei Index," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 5(2), pages 99-128, May.
    10. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    11. Peter Hansen & Asger Lunde, 2003. "Consistent Preordering with an Estimated Criterion Function, with an Application to the Evaluation and Comparison of Volatility Models," Working Papers 2003-01, Brown University, Department of Economics.
    12. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters,in: THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78 World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Ioannis A. Tampakoudis & Demetres N. Subeniotis & Ioannis G. Kroustalis, 2012. "Modelling volatility during the current financial crisis: an empirical analysis of the US and the UK stock markets," International Journal of Trade and Global Markets, Inderscience Enterprises Ltd, vol. 5(3/4), pages 171-194.

    More about this item


    Conditional heteroskedasticity; Multiple regimes; Trading volume; Estimation; Forecasting; C22; C51; C54; G15;

    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
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets


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