Non-Gaussian quasi-likelihood estimation of SDE driven by locally stable Lévy process

Hiroki Masuda

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    13 Citations (Scopus)

    Abstract

    We address estimation of parametric coefficients of a pure-jump Lévy driven univariate stochastic differential equation (SDE) model, which is observed at high frequency over a fixed time period. It is known from the previous study (Masuda, 2013) that adopting the conventional Gaussian quasi-maximum likelihood estimator then leads to an inconsistent estimator. In this paper, under the assumption that the driving Lévy process is locally stable, we extend the Gaussian framework into a non-Gaussian counterpart, by introducing a novel quasi-likelihood function formally based on the small-time stable approximation of the unknown transition density. The resulting estimator turns out to be asymptotically mixed normally distributed without ergodicity and finite moments for a wide range of the driving pure-jump Lévy processes, showing much better theoretical performance compared with the Gaussian quasi-maximum likelihood estimator. Extensive simulations are carried out to show good estimation accuracy. The case of large-time asymptotics under ergodicity is briefly mentioned as well, where we can deduce an analogous asymptotic normality result.

    Original languageEnglish
    Pages (from-to)1013-1059
    Number of pages47
    JournalStochastic Processes and their Applications
    Volume129
    Issue number3
    DOIs
    Publication statusPublished - Mar 2019

    All Science Journal Classification (ASJC) codes

    • Statistics and Probability
    • Modelling and Simulation
    • Applied Mathematics

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