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

The Poisson process is an essential building block to move up to complicated counting
processes, such as the Cox (“doubly stochastic Poisson”) process, the Hawkes (“self …

This paper characterizes the dynamics of jumps and analyzes their importance for volatility
forecasting. Using high‐frequency data on four prominent energy markets, we perform a …

A brief history of diffusions in Finance is presented, followed by an even briefer discussion of
jump-diffusions that involve Poisson or Lévy jumps. The main purpose of the paper is then to …

This paper examines the forecasting performances of high-frequency jump tests for oil
futures volatility from a comprehensive perspective. It contributes to the literature by …

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

Price limits are widely implemented in stock markets worldwide; however, they are rarely
considered in financial models. In this study, we propose a model specifically designed for …

There has been much recent interest in modifying Bayesian inference for misspecified
models so that it is useful for specific purposes. One popular modified Bayesian inference …

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 …

This paper analyzes the efficiency of hedging strategies for stock options in the presence of
jump clustering. In the proposed model, the asset is ruled by a jump-diffusion process …