The aim of this survey paper is to provide an account of some of the important developments
in the autoregressive conditional heteroskedasticity (ARCH) model since its inception in a …

Publisher Summary This chapter reviews modeling time-varying volatility using generalized
autoregressive conditional heteroskedastic (GARCH) processes. The presence of excess …

Provides a comprehensive and updated study of GARCH models and their applications in
finance, covering new developments in the discipline This book provides a comprehensive …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We
show that identification problems are alleviated when variation in factor variances is …

Diagnostic checking is an important step in the modeling process. But while the literature on
diagnostic checks is quite extensive and many texts on time series modeling are available, it …

An up-to-date approach to understanding statistical inference Statistical inference is finding
useful applications in numerous fields, from sociology and econometrics to biostatistics. This …

We conduct an extensive empirical analysis of VIX derivative valuation models before,
during, and after the 2008–2009 financial crisis. Since the restrictive mean-reversion and …

Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to
model asset price volatility over time. This book introduces both the theory and applications …

In this paper, a unified framework for testing the adequacy of an estimated GARCH model is
presented. Parametric Lagrange multiplier (LM) or LM type tests of no ARCH in standardized …