Exponential Conditional Volatility Models
نویسنده
چکیده
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. Keywords: Duration models; gamma distribution; general error distribution; heteroskedasticity; leverage; score; Student's t. * Faculty of Economics, Cambridge University, Sidgwick Avenue, Cambridge CB3 9DD, England, e-mail: [email protected] Exponential Conditional Volatility Models Andrew Harvey Faculty of Economics, Cambridge University [email protected] October 4, 2010 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. KEYWORDS: Duration models; gamma distribution; general error distribution; heteroskedasticity; leverage; score; Students t. JEL classi cation; C22, G17The 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. KEYWORDS: Duration models; gamma distribution; general error distribution; heteroskedasticity; leverage; score; Students t. JEL classi cation; C22, G17
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