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

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  • Masuda, Hiroki

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.

Suggested Citation

  • Masuda, Hiroki, 2019. "Non-Gaussian quasi-likelihood estimation of SDE driven by locally stable Lévy process," Stochastic Processes and their Applications, Elsevier, vol. 129(3), pages 1013-1059.
  • Handle: RePEc:eee:spapps:v:129:y:2019:i:3:p:1013-1059
    DOI: 10.1016/j.spa.2018.04.004
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    1. Clément, Emmanuelle & Gloter, Arnaud, 2015. "Local Asymptotic Mixed Normality property for discretely observed stochastic differential equations driven by stable Lévy processes," Stochastic Processes and their Applications, Elsevier, vol. 125(6), pages 2316-2352.
    2. Masuda, Hiroki, 2013. "Asymptotics for functionals of self-normalized residuals of discretely observed stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2752-2778.
    3. Jianqing Fan & Lei Qi & Dacheng Xiu, 2014. "Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 178-191, April.
    4. Michael Grabchak & Gennady Samorodnitsky, 2010. "Do financial returns have finite or infinite variance? A paradox and an explanation," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 883-893.
    5. Yacine Aït-Sahalia & Jean Jacod, 2014. "High-Frequency Financial Econometrics," Economics Books, Princeton University Press, edition 1, number 10261.
    6. Tommaso Costa & Giuseppe Boccignone & Franco Cauda & Mario Ferraro, 2016. "The Foraging Brain: Evidence of Lévy Dynamics in Brain Networks," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-16, September.
    7. Mizera, Ivan & Müller, Christine H., 2002. "Breakdown points of Cauchy regression-scale estimators," Statistics & Probability Letters, Elsevier, vol. 57(1), pages 79-89, March.
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    Cited by:

    1. Alessandro Gregorio & Francesco Iafrate, 2021. "Regularized bridge-type estimation with multiple penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 921-951, October.
    2. Masahiro Kurisaki, 2023. "Parameter estimation for ergodic linear SDEs from partial and discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 279-330, July.
    3. Alexander Gushchin & Ilya Pavlyukevich & Marian Ritsch, 2020. "Drift estimation for a Lévy-driven Ornstein–Uhlenbeck process with heavy tails," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 553-570, October.
    4. Li, Shuaiyu & Wu, Yunpei & Cheng, Yuzhong, 2024. "Parameter estimation and random number generation for student Lévy processes," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).

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