Probabilistic forecasts of continuous variables take the form of predictive densities or
predictive cumulative distribution functions. We propose a diagnostic approach to the …

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

This paper discusses and documents G@ RCH 2.2, an Ox package dedicated to the
estimation and forecast of various univariate ARCH–type models including GARCH …

This paper introduces the logarithmic autoregressive conditional duration (Log-ACD) model
and compares it with the ACD model of Engle and Russell [1998]. The logarithmic version …

Bayesian inference in a time series model provides exact out-of-sample predictive
distributions that fully and coherently incorporate parameter uncertainty. This study …

Data collection at ultra high-frequency on financial markets requires the manipulation of
complex databases, and possibly the correction of errors present in the data. The New York …

Analyzing cyber incident data sets is an important method for deepening our understanding
of the evolution of the threat situation. This is a relatively new research topic, and many …

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 …

We introduce a class of models for the analysis of durations, which we call stochastic
conditional duration (SCD) models. These models are based on the assumption that the …

This paper shows that the monotonicity of the conditional hazard in traditional ACD models
is both econometrically important and empirically invalid. To counter this problem we …