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
Autoregressive Conditional Heteroskedasticity (ARCH) models have been applied in modeling the relati...
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponentia...
In conditionally heteroskedastic models, the optimal prediction of powers, or logarithms, of the abs...
The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential ...
The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential g...
The paper considers various extended asymmetric multivariate conditional volatility models, and deri...
One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH...
Recently, volatility modeling has been a very active and extensive research area in empirical financ...
A time series model in which the signal is buried in noise that is non-Gaussian may throw up observa...
A new semiparametric observation-driven volatility model is proposed. In contrast to the standard se...
Exponential models of autoregressive conditional heteroscedasticity (ARCH) are attractive in empiric...
This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Co...
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponentia...
Autoregressive Conditional Heteroskedasticity (ARCH) models have been applied in modeling the relati...
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponentia...
In conditionally heteroskedastic models, the optimal prediction of powers, or logarithms, of the abs...
The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential ...
The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential g...
The paper considers various extended asymmetric multivariate conditional volatility models, and deri...
One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH...
Recently, volatility modeling has been a very active and extensive research area in empirical financ...
A time series model in which the signal is buried in noise that is non-Gaussian may throw up observa...
A new semiparametric observation-driven volatility model is proposed. In contrast to the standard se...
Exponential models of autoregressive conditional heteroscedasticity (ARCH) are attractive in empiric...
This paper sets up a statistical framework for modeling realised volatility (RV ) using a Dynamic Co...
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponentia...
Autoregressive Conditional Heteroskedasticity (ARCH) models have been applied in modeling the relati...
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponentia...
In conditionally heteroskedastic models, the optimal prediction of powers, or logarithms, of the abs...
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