Efficient posterior inference on the volatility of a jump diffusion process

TL;DR

This paper develops an efficient Bayesian method for inferring the volatility of jump diffusion processes from discrete data, using a purposely misspecified model that ignores jumps, and demonstrates asymptotic efficiency of the resulting posterior.

Contribution

It introduces a simplified Bayesian approach that bypasses jump modeling, providing asymptotically efficient inference on volatility despite model misspecification.

Findings

Modified posterior achieves asymptotic efficiency.

Method effectively ignores jump components without loss of accuracy.

Asymptotic variance matches the Cramér-Rao bound for no-jumps model.

Abstract

Jump diffusion processes are widely used to model asset prices over time, mainly for their ability to capture complex discontinuous behavior, but inference on the model parameters remains a challenge. Here our goal is posterior inference on the volatility coefficient of the diffusion part of the process based on discrete samples. A Bayesian approach requires specification of a model for the jump part of the process, prior distributions for the corresponding parameters, and computation of the joint posterior. Since the volatility coefficient is our only interest, it would be desirable to avoid the modeling and computational costs associated with the jump part of the process. Towards this, we consider a {\em purposely misspecified model} that ignores the jump part entirely. We work out precisely the asymptotic behavior of the Bayesian posterior under the misspecified model, propose some simple modifications to correct for the effects of misspecification, and demonstrate that our modified posterior inference on the volatility is efficient in the sense that its asymptotic variance equals the no-jumps model Cram\'er--Rao bound.