Least squares volatility change point estimation for partially observed diffusion processes

TL;DR

This paper develops a least squares method to detect change points in volatility regimes of partially observed diffusion processes, providing consistency and convergence results under high-frequency sampling.

Contribution

It introduces a novel least squares approach for estimating volatility change points in diffusion processes with known or unknown parameters, including theoretical properties.

Findings

Consistent estimation of change point $t^*$.

Derived rates of convergence for estimators.

Distributional results under high-frequency sampling.

Abstract

A one dimensional diffusion process $X=\{X_t, 0\leq t \leq T\}$, with drift $b(x)$ and diffusion coefficient $\sigma(\theta, x)=\sqrt{\theta} \sigma(x)$ known up to $\theta>0$, is supposed to switch volatility regime at some point $t^*\in (0,T)$. On the basis of discrete time observations from $X$, the problem is the one of estimating the instant of change in the volatility structure $t^*$ as well as the two values of $\theta$, say $\theta_1$ and $\theta_2$, before and after the change point. It is assumed that the sampling occurs at regularly spaced times intervals of length $\Delta_n$ with $n\Delta_n=T$. To work out our statistical problem we use a least squares approach. Consistency, rates of convergence and distributional results of the estimators are presented under an high frequency scheme. We also study the case of a diffusion process with unknown drift and unknown volatility but constant.