Estimating Diffusion With Compound Poisson Jumps Based On Self-normalized Residuals

TL;DR

This paper introduces a practical, threshold-free method for estimating the continuous component of ergodic diffusions with jumps using high-frequency data, leveraging the Jarque-Bera normality test on residuals.

Contribution

It proposes a novel, data-adapted estimation technique based on normality testing that avoids sensitive threshold tuning, achieving asymptotic efficiency.

Findings

The method is asymptotically equivalent to an ideal estimator.

It performs well in finite samples according to numerical experiments.

The approach simplifies jump detection without sacrificing accuracy.

Abstract

We consider parametric estimation of the continuous part of a class of ergodic diffusions with jumps based on high-frequency samples. Various papers previously proposed threshold based methods, which enable us to distinguish whether observed increments have jumps or not at each small-time interval, hence to estimate the unknown parameters separately. However, a data-adapted and quantitative choice of the threshold parameter is known to be a subtle and sensitive problem. In this paper, we present a simple alternative based on the Jarque-Bera normality test for the Euler residuals. Different from the threshold based method, the proposed method does not require any sensitive fine tuning, hence is of practical value. It is shown that under suitable conditions the proposed estimator is asymptotically equivalent to an estimator constructed by the unobserved fluctuation of the continuous part of the solution process, hence is asymptotically efficient. Some numerical experiments are conducted to observe finite-sample performance of the proposed method.