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Exponential conditional volatility models

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Listed author(s):
  • 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.

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File URL: http://e-archivo.uc3m.es/bitstream/handle/10016/9343/ws103620.pdf?sequence=1
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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws103620.

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Date of creation: Sep 2010
Handle: RePEc:cte:wsrepe:ws103620
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

Keywords: Score;

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

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  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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