On the nonparametric inference of coefficients of self-exciting jump-diffusion

TL;DR

This paper develops nonparametric methods to estimate volatility and jump functions in a jump-diffusion process driven by a Hawkes process, addressing a previously open problem with theoretical guarantees and practical simulations.

Contribution

It introduces new estimators for volatility and jump functions in jump-diffusion models with high-frequency data, providing consistency, convergence rates, and adaptive procedures.

Findings

Establishes bounds for empirical risk of estimators.

Demonstrates consistency and adaptivity of the proposed estimators.

Provides a methodology for recovering jump functions in applications.

Abstract

In this paper, we consider a one-dimensional diffusion process with jumps driven by a Hawkes process. We are interested in the estimations of the volatility function and of the jump function from discrete high-frequency observations in a long time horizon which remained an open question until now. First, we propose to estimate the volatility coefficient. For that, we introduce a truncation function in our estimation procedure that allows us to take into account the jumps of the process and estimate the volatility function on a linear subspace of L2(A) where A is a compact interval of R. We obtain a bound for the empirical risk of the volatility estimator, ensuring its consistency, and then we study an adaptive estimator w.r.t. the regularity. Then, we define an estimator of a sum between the volatility and the jump coefficient modified with the conditional expectation of the intensity of the jumps. We also establish a bound for the empirical risk for the non-adaptive estimators of this sum, the convergence rate up to the regularity of the true function, and an oracle inequality for the final adaptive estimator.Finally, we give a methodology to recover the jump function in some applications. We conduct a simulation study to measure our estimators' accuracy in practice and discuss the possibility of recovering the jump function from our estimation procedure.