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Parameter estimation based on discrete observations of fractional Ornstein–Uhlenbeck process of the second kind

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  • Ehsan Azmoodeh
  • Lauri Viitasaari

Abstract

Fractional Ornstein–Uhlenbeck process of the second kind $$(\text {fOU}_{2})$$ ( fOU 2 ) is a solution of the Langevin equation $$\mathrm {d}X_t=-\theta X_t\,\mathrm {d}t+\mathrm {d}Y_t^{(1)}, \ \theta >0$$ d X t = - θ X t d t + d Y t ( 1 ) , θ > 0 with a Gaussian driving noise $$ Y_t^{(1)} := \int ^t_0 e^{-s} \,\mathrm {d}B_{a_s}$$ Y t ( 1 ) : = ∫ 0 t e - s d B a s , where $$ a_t= H e^{\frac{t}{H}}$$ a t = H e t H and $$B$$ B is a fractional Brownian motion with Hurst parameter $$H \in (0,1)$$ H ∈ ( 0 , 1 ) . In this article we consider the case $$H>\frac{1}{2}$$ H > 1 2 , and by using the ergodicity of $$\text {fOU}_{2}$$ fOU 2 process we construct consistent estimators for the drift parameter $$\theta $$ θ based on discrete observations in two possible cases: $$(i)$$ ( i ) the Hurst parameter $$H$$ H is known and $$(ii)$$ ( i i ) the Hurst parameter $$H$$ H is unknown. Moreover, using Malliavin calculus techniques we prove central limit theorems for our estimators which are valid for the whole range $$H \in (\frac{1}{2},1)$$ H ∈ ( 1 2 , 1 ) . Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Ehsan Azmoodeh & Lauri Viitasaari, 2015. "Parameter estimation based on discrete observations of fractional Ornstein–Uhlenbeck process of the second kind," Statistical Inference for Stochastic Processes, Springer, vol. 18(3), pages 205-227, October.
  • Handle: RePEc:spr:sistpr:v:18:y:2015:i:3:p:205-227
    DOI: 10.1007/s11203-014-9111-8
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    References listed on IDEAS

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    1. Nualart, D. & Ortiz-Latorre, S., 2008. "Central limit theorems for multiple stochastic integrals and Malliavin calculus," Stochastic Processes and their Applications, Elsevier, vol. 118(4), pages 614-628, April.
    2. Jean-François Coeurjolly, 2001. "Estimating the Parameters of a Fractional Brownian Motion by Discrete Variations of its Sample Paths," Statistical Inference for Stochastic Processes, Springer, vol. 4(2), pages 199-227, May.
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    Cited by:

    1. Es-Sebaiy, Khalifa & Viens, Frederi G., 2019. "Optimal rates for parameter estimation of stationary Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3018-3054.
    2. Tommi Sottinen & Lauri Viitasaari, 2018. "Parameter estimation for the Langevin equation with stationary-increment Gaussian noise," Statistical Inference for Stochastic Processes, Springer, vol. 21(3), pages 569-601, October.
    3. Marko Voutilainen & Lauri Viitasaari & Pauliina Ilmonen & Soledad Torres & Ciprian Tudor, 2022. "Vector‐valued generalized Ornstein–Uhlenbeck processes: Properties and parameter estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 992-1022, September.
    4. Pavel Kříž & Leszek Szała, 2020. "Least-Squares Estimators of Drift Parameter for Discretely Observed Fractional Ornstein–Uhlenbeck Processes," Mathematics, MDPI, vol. 8(5), pages 1-20, May.

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