A new class of multivariate threshold GARCH models is proposed for the analysis and modelling of volatility asymmetries in financial time series. The approach is based on the idea of a binary tree where every terminal node parameterizes a (local) multivariate GARCH model for a specific partition...

Behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We...

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo...

We deal with real data from a stated preference experiment which was designed to explain and predict passengers' behaviour towards three main means of transportation in the city of Athens. The resulting model formulations give rise to the so-called multiranked probit model which emerges from a...

We develop Markov chain Monte Carlo methodology for Bayesian inference for non-Gaussian Ornstein-Uhlenbeck stochastic volatility processes. The approach introduced involves expressing the unobserved stochastic volatility process in terms of a suitable marked Poisson process. We introduce two...

We develop threshold models that allow volatilities and copula functions or their association parameters to change across time. The number and location of the thresholds is assumed unknown. We use a Markov chain Monte Carlo strategy combined with Laplace estimates that evaluate the required...