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

Abstract Approximate Bayesian Computation (ABC) has become increasingly prominent as
a method for conducting parameter inference in a range of challenging statistical problems …

Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes
process in conjunction with a bivariate jump diffusion. A state‐space representation is used …

We conduct an extensive evaluation of price jump tests based on high-frequency financial
data. After providing a concise review of multiple alternative tests, we document the size and …

We find that factors explaining bank loan recovery rates differ depending on the state of an
underlying credit cycle. Our modelling approach incorporates a two-state Markov switching …

As evidenced by an extensive empirical literature, multiplicative error models (MEM) show
good performance in capturing the stylized facts of nonnegative time series; examples …

We estimate variance risk premiums (VRPs) in stock markets of selected major advanced
economies (AEs) and emerging market economies (EMEs) over 2007–2015, and …

Splines are useful building blocks when constructing priors on nonparametric models
indexed by functions. Recently it has been established in the literature that hierarchical …

A new modular approximate Bayesian inferential framework is proposed that enables fast
calculation of probabilistic predictions of future option prices. We exploit multiple information …

The object of this paper is to produce non-parametric maximum likelihood estimates of
forecast distributions in a general non-Gaussian, non-linear state space setting. The …