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Exponential conditional volatility models

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  • Harvey, Andrew

Abstract

The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponential link function. A key feature of the model formulation is that the dynamics are driven by the score.

Suggested Citation

  • Harvey, Andrew, 2010. "Exponential conditional volatility models," DES - Working Papers. Statistics and Econometrics. WS ws103620, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws103620
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    References listed on IDEAS

    as
    1. González-Rivera, Gloria & Senyuz, Zeynep & Yoldas, Emre, 2011. "Autocontours: Dynamic Specification Testing," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 186-200.
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    Cited by:

    1. Wintenberger, Olivier & Cai, Sixiang, 2011. "Parametric inference and forecasting in continuously invertible volatility models," MPRA Paper 31767, University Library of Munich, Germany.
    2. Bernd Schwaab, 2012. "Conditional probabilities and contagion measures for euro area sovereign default risk," Research Bulletin, European Central Bank, vol. 17, pages 6-11.

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    More about this item

    Keywords

    Student's t;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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