In many applications, it has been found that the autoregressive conditional
heteroskedasticity (ARCH) model under the conditional normal or Student'st distributions are …

This paper develops a flexible parametric approach to capture asymmetry and excess
kurtosis along with conditional heteroskedasticity with a general family of distributions for …

The 20th century began on an auspicious statistical note with the publication of Karl
Pearson's (Philos. Mag. Ser. 50 (1900) 157) goodness-of-fit test, which is regarded as one of …

Although the generalised autoregressive conditional heteroskedasticity (GARCH) model has
been quite successful in capturing important empirical aspects of financial data, particularly …

Agricultural producers face a wide array of risks that influence their production and
marketing decisions. Though a precise and comprehensive taxonomy may be difficult, risk is …

This paper derives asymptotic normality of a class of M-estimators in the generalized
autoregressive conditional heteroskedastic (GARCH) model. The class of estimators …

The idea of using estimating functions goes a long way back, at least to Karl Pearson's
introduction to the method of moments in 1894. It is now a very active area of research in the …

Estimating functions provide a very general framework for the statistical inference of dynamic
models under weak assumptions. We consider a class of time series models consisting in …

There is now a massive literature on both the GARCH family of risk models and the related
Auto-Conditional Duration (ACD) models used for modeling the stochastic timing of trades or …

Abstract We construct a Generalized Empirical Likelihood estimator for a GARCH (1, 1)
model with a possibly heavy tailed error. The estimator imbeds tail-trimmed estimating …