Abstract The coronavirus (Covid-19) pandemic created a shock not only for the health-care
industry but also the global economy and finances. The pandemic also caused an increase …

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 …

The volatility of financial returns changes over time and, for the last thirty years, Generalized
Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal …

We introduce a new framework, Realized GARCH, for the joint modeling of returns and
realized measures of volatility. A key feature is a measurement equation that relates the …

In this article we consider geometric ergodicity and likelihood-based inference for linear and
nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly …

We consider a log-linear model for time series of counts. This type of model provides a
framework where both negative and positive association can be taken into account. In …

Novel periodic extensions of dynamic long-memory regression models with autoregressive
conditional heteroscedastic errors are considered for the analysis of daily electricity spot …

An EGARCH model in which the conditional distribution is heavy-tailed and skewed is
proposed. The properties of the model, including unconditional moments, autocorrelations …

We investigate the empirical relevance of structural breaks for GARCH models of exchange
rate volatility using both in‐sample and out‐of‐sample tests. We find significant evidence of …

An unobserved components model in which the signal is buried in noise that is non-
Gaussian may throw up observations that, when judged by the Gaussian yardstick, are …