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Journal Articles (Preprint) Electronic Journal of Statistics Year : 2022

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

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.
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Dates and versions

hal-03021151 , version 1 (24-11-2020)
hal-03021151 , version 2 (18-06-2021)
hal-03021151 , version 3 (25-04-2022)

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Chiara Amorino, Charlotte Dion, Arnaud Gloter, Sarah Lemler. On the nonparametric inference of coefficients of self-exciting jump-diffusion. Electronic Journal of Statistics , 2022, 16. ⟨hal-03021151v3⟩
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