Copula functions have become standard practice for pricing multi-name credit derivatives. Marginal default distributions are often chosen by using a simple deterministic intensity function. It is wellknown that this approach only generates default time correlation and, apart from jumps due to default events, does not generate correlation between the conditional default intensities, or the condi...

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is ...

We analyzed the volatility dynamics of three developed markets (U.K., U.S. and Japan), during the period 2003-2011, by comparing the performance of several multivariate volatility models, namely Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC) and consistent DCC (cDCC) models. To evaluate the performance of models we used four statistical loss functions on the daily...

A multivariate stochastic volatility model with the dynamic correlation and the cross leverage effect is described and its efficient estimation method using Markov chain Monte Carlo is proposed. The time-varying covariance matrices are guaranteed to be positive definite by using a matrix exponential transformation. Of particular interest is our approach for sampling a set of latent matrix logar...

A small strand of recent literature is occupied with identifying simultaneity in multiple equation systems through autoregressive conditional heteroscedasticity. Since this approach assumes that the structural innovations are uncorrelated, any contemporaneous connection of the endogenous variables needs to be exclusively explained by mutual spillover effects. In contrast, this paper allows for ...

Longitudinal and survival sub-models are two building blocks for joint modelling of longitudinal time-to-event data. Extensive research indicates separate analysis these processes could result in biased outputs due to their associations. Conditional independence between measurements biomarkers event time process given latent classes or random effects is a conventional approach characterising th...