The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

This paper provides an up‐to‐date survey of the main theoretical developments in
autoregressive conditional duration (ACD) modeling and empirical studies using financial …

We survey the modelling of financial markets transaction data characterized by irregular
spacing in time, in particular so-called financial durations. We begin by reviewing the …

This study estimates the returns and volatility in the China's stock index futures market. Our
approach introduces a novel consideration of price duration, a factor that we integrate into …

Prefetching is a simple and general method for single-chain parallelisation of the Metropolis–
Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of …

I introduce the HESSIAN (highly efficient simulation smoothing in a nutshell) method for
numerically efficient simulation smoothing in state space models with univariate states …

Abstract Approximate Bayesian Computation (ABC) has gained popularity as a method for
conducting inference and forecasting in complex models, most notably those which are …

The evaluation of the likelihood function of the stochastic conditional duration (SCD) model
requires to compute an integral that has the dimension of the sample size. ML estimation …

The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte
Carlo (MCMC) algorithms for two non-Gaussian state space models is examined …

Multiplicative Error Models (MEM) can be used to trace the dynamics of non-negative valued
processes. Interactions between several such processes are accommodated by the vector …