Estimation of the jump processes

Abstract : In this thesis, we consider a stochastic differential equation driven by a truncated pure jump Lévy process with index α ∈(0,2) and observe high frequency data of the process on a fixed observation time. We first study the behavior of the density of the process in small time. Next, we prove the Local Asymptotic Mixed Normality (LAMN) property for the drift and scaling parameters from high frequency observations. Finally, we propose some estimators of the index parameter of the process.The first part deals with the asymptotic behavior of the density in small time of the process. The process is assumed to depend on a parameter β = (θ,σ) and we study, in this part, the sensitivity of the density with respect to this parameter. This extends the results of [17] which were restricted to the index α ∈ (1,2) and considered only the sensitivity with respect to the drift coefficient. By using Malliavin calculus, we obtain the representation of the density, its derivative and its logarithm derivative as an expectation and a conditional expectation. These representation formulas involve some Malliavin weights whose expressions are given explicitly and this permits to analyze the asymptotic behavior in small time of the density, using the self-similarity property of the stable process.The second part of this thesis concerns the Local Asymptotic Mixed Normality property for the parameters. Both the drift coefficient and scale coefficient depend on the unknown parameters. Extending the results of [17], we compute the asymptotic Fisher information and find that the rate in the Local Asymptotic Mixed Normality property depends on the index α.The third part proposes some estimators of the jump activity index α ∈ (0,2) based on the method of moments as in Masuda [53]. We prove the consistency and asymptotic normality of the estimators and give some simulations to illustrate the finite-sample behaviors of the estimators
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Thi Thu Huong Nguyen. Estimation of the jump processes. General Mathematics [math.GM]. Université Paris-Est, 2018. English. ⟨NNT : 2018PESC1124⟩. ⟨tel-02127797⟩

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