In this article we present a theoretical review of the existing literature on Value at Risk (VaR)
specifically focussing on the development of new approaches for its estimation. We effect a …

This paper provides a review of some recent theoretical results for time series models with
GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model …

Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

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 …

The use of Markov-switching models to capture the volatility dynamics of financial time
series has grown considerably during past years, in part because they give rise to a …

The literature on modeling and forecasting time-varying volatility is ripe with acronyms and
abbreviations used to describe the many different parametric models that have been put …

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 …

Both unconditional mixed normal distributions and GARCH models with fat-tailed conditional
distributions have been employed in the literature for modeling financial data. We consider a …

We use a semiparametric Markov switching AR-ARCH model to forecast the prices of OPEC,
WTI, and Brent crude oils. We investigate the applicability of this model based on the proper …