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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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Preprints, Working Papers, ...
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Contributor : Sarah Lemler Connect in order to contact the contributor
Submitted on : Monday, April 25, 2022 - 12:39:35 PM
Last modification on : Friday, April 29, 2022 - 3:35:40 AM


  • HAL Id : hal-03021151, version 3
  • ARXIV : 2011.12387


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



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