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 paper addresses the question of the selection of multivariate generalized
autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix …

Heteroskedasticity is a common feature of financial time series and is commonly addressed
in the model building process through the use of autoregressive conditional heteroskedastic …

Estimation of time-varying covariances is a key input in risk management and asset
allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of …

This paper presents the R package GAS for the analysis of time series under the
generalized autoregressive score (GAS) framework of Creal, Koopman, and Lucas (2013) …

Numerous models have been proposed for count time series, including the integer-valued
autoregressive moving average (ARMA) and integer-valued generalized autoregressive …

We extend the GARCH–MIDAS model to take into account possible different impacts from
positive and negative macroeconomic variations on financial market volatility: a Monte Carlo …

The maximum likelihood estimator applied to the dynamic conditional correlation model is
severely biased in high dimensions. This is, in particular, the case where the cross-section …

Large one-off events cause large changes in prices, but may not affect the volatility and
correlation dynamics as much as smaller events. In such cases, standard volatility models …

Trading by commodity index traders (CITs) has become an important aspect of financial
markets over the past 10 years. We develop an equilibrium model of trader behavior that …