We study a mixed hitting-time (MHT) model that specifies durations as the first time a Lévy process - a continuous-time process with stationary and independent increments - crosses a heterogeneous threshold. Such models are of substantial interest because they can be reduced from...

For a time-continuous discrete-state Markov process as model for rating transitions, we study the time-stationarity by means of a likelihood ratio test. For multiple Markov process data from a multiplicative intensity model, maximum likelihood parameter estimates can be represented as martingale...

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density...

This paper generalizes the ACD models of Engle and Russell (1998) using the so-called q-Weibull distribution as the conditional distribution. The new specification allows the hazard function to be non-monotonic. We document that the q-Weibull distribution recently suggested in physics as a...

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...

This paper proposes a threshold stochastic conditional duration (SCD) model for financial data at the transaction level. In addition to assuming that the innovations of the duration process follow a threshold distribution with positive support, we also assume that the latent first-order...

This paper studies stochastic conditional duration models with a mixture of distribution processes for financial asset's transaction data. The mixture component distributions include exponential, gamma and Weibull. The models allow for a correlation between the observed durations and the...

This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable...

This paper proposes a threshold stochastic conditional duration (TSCD) model to capture the asymmetric property of financial transactions. The innovation of the observable duration equation is assumed to follow a threshold distribution with two component distributions switching between two...

This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that the latent...