Full Bayesian analysis for a class of jump-diffusion models

TL;DR

This paper introduces a Bayesian significance test tailored for detecting jumps in diffusion processes, especially useful for analyzing temporal data with extreme outliers such as financial or geophysical records.

Contribution

It develops a novel Bayesian jump detection method specifically designed for jump-diffusion models, improving analysis of outlier-prone temporal data.

Findings

Effective detection of jumps in financial data.

Applicable to various fields with extreme outliers.

Enhanced accuracy over traditional methods.

Abstract

A new Bayesian significance test is adjusted for jump detection in a diffusion process. This is an advantageous procedure for temporal data having extreme valued outliers, like financial data, pluvial or tectonic forces records and others.