This paper provides theoretical properties and Monte-Carlo studies of a stochastic conditional duration model with mixture-of-normal error distributions an effcient estimation approach via a continuous empirical characteristic function. The empirical version of this paper is studied in Xu,...

This paper constructs Value at Risk (VaR) measures from a stochastic volatility model with a discrete bivariate mixture-of-normal error distribution - henceforth SV-MN. This volatility-gnerating model is able to accommodate many of the salient features of financial asset returns, such as...

This paper extends the stochastic conditional duration model first proposed by Bauwens and Veredas (2004) by imposing mixtures of bivariate normal distributions on the innovations of the observation and latent equations of the duration process. This extension allows the model not only to capture...

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution...

This paper extends the multiscale stochastic volatility (MSSV) models to allow for heavy tails of the marginal distribution of the asset returns and correlation between the innovation of the mean equation and the innovations of the latent factor processes. Novel algorithms of Markov Chain Monte...

In this paper we revisit the notion that a single factor of duration running on single time scale is adequate to capture the dynamics of the duration process of financial transaction data. The documented poor fit of the left tail of the marginal distribution of the observed durations in some...