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Asymptotic properties of estimators in a stable Cox–Ingersoll–Ross model

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  • Li, Zenghu
  • Ma, Chunhua

Abstract

We study the estimation of a stable Cox–Ingersoll–Ross model, which is a special subcritical continuous-state branching process with immigration. The exponential ergodicity and strong mixing property of the process are proved by a coupling method. The regular variation properties of distributions of the model are studied. The key is to establish the convergence of some point processes and partial sums associated with the model. From those results, we derive the consistency and central limit theorems of the conditional least squares estimators (CLSEs) and the weighted conditional least squares estimators (WCLSEs) of the drift parameters based on low frequency observations. The theorems show that the WCLSEs are more efficient than the CLSEs and their errors have distinct decay rates n−(α−1)/α and n−(α−1)/α2, respectively, as the sample sizes n goes to infinity. The arguments depend heavily on the recent results on the construction and characterization of the model in terms of stochastic equations.

Suggested Citation

  • Li, Zenghu & Ma, Chunhua, 2015. "Asymptotic properties of estimators in a stable Cox–Ingersoll–Ross model," Stochastic Processes and their Applications, Elsevier, vol. 125(8), pages 3196-3233.
    • Handle: RePEc:eee:spapps:v:125:y:2015:i:8:p:3196-3233
    DOI: 10.1016/j.spa.2015.03.002
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    2. Martin Friesen & Sven Karbach, 2024. "Stationary covariance regime for affine stochastic covariance models in Hilbert spaces," Finance and Stochastics, Springer, vol. 28(4), pages 1077-1116, October.
    3. Hui He & Zenghu Li & Wei Xu, 2018. "Continuous-State Branching Processes in Lévy Random Environments," Journal of Theoretical Probability, Springer, vol. 31(4), pages 1952-1974, December.
    4. Shen, Leyi & Xia, Xiaoyu & Yan, Litan, 2022. "Least squares estimation for the linear self-repelling diffusion driven by α-stable motions," Statistics & Probability Letters, Elsevier, vol. 181(C).
    5. Ying Jiao & Chunhua Ma & Simone Scotti, 2017. "Alpha-CIR model with branching processes in sovereign interest rate modeling," Finance and Stochastics, Springer, vol. 21(3), pages 789-813, July.
    6. Mohamed Ben Alaya & Martin Friesen & Jonas Kremer, 2024. "Ergodicity and Law-of-large numbers for the Volterra Cox-Ingersoll-Ross process," Papers 2409.04496, arXiv.org.
    7. Giorgia Callegaro & Andrea Mazzoran & Carlo Sgarra, 2019. "A Self-Exciting Modelling Framework for Forward Prices in Power Markets," Papers 1910.13286, arXiv.org.
    8. Shukai Chen & Rongjuan Fang & Xiangqi Zheng, 2023. "Wasserstein-Type Distances of Two-Type Continuous-State Branching Processes in Lévy Random Environments," Journal of Theoretical Probability, Springer, vol. 36(3), pages 1572-1590, September.
    9. Aur'elien Alfonsi & Guillaume Szulda, 2024. "On non-negative solutions of stochastic Volterra equations with jumps and non-Lipschitz coefficients," Papers 2402.19203, arXiv.org, revised Jul 2024.
    10. Ying Jiao & Chunhua Ma & Simone Scotti & Chao Zhou, 2018. "The Alpha-Heston Stochastic Volatility Model," Papers 1812.01914, arXiv.org.
    11. Ying Jiao & Chunhua Ma & Simone Scotti & Carlo Sgarra, 2019. "A branching process approach to power markets," Post-Print hal-02954986, HAL.
    12. Martin Friesen & Peng Jin & Jonas Kremer & Barbara Rüdiger, 2023. "Regularity of transition densities and ergodicity for affine jump‐diffusions," Mathematische Nachrichten, Wiley Blackwell, vol. 296(3), pages 1117-1134, March.
    13. Mátyás Barczy & Kristóf Körmendi & Gyula Pap, 2016. "Statistical inference for critical continuous state and continuous time branching processes with immigration," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(7), pages 789-816, October.
    14. Jiao, Ying & Ma, Chunhua & Scotti, Simone & Sgarra, Carlo, 2019. "A branching process approach to power markets," Energy Economics, Elsevier, vol. 79(C), pages 144-156.
    15. Fabian Mies & Ansgar Steland, 2019. "Nonparametric Gaussian inference for stable processes," Statistical Inference for Stochastic Processes, Springer, vol. 22(3), pages 525-555, October.
    16. Yurong Pan & Litan Yan, 2019. "The Least Squares Estimation for the α-Stable Ornstein-Uhlenbeck Process with Constant Drift," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1165-1182, December.
    17. Fontana, Claudio & Gnoatto, Alessandro & Szulda, Guillaume, 2023. "CBI-time-changed Lévy processes," Stochastic Processes and their Applications, Elsevier, vol. 163(C), pages 323-349.
    18. Yang, Xu, 2017. "Maximum likelihood type estimation for discretely observed CIR model with small α-stable noises," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 18-27.
    19. Ying Jiao & Chunhua Ma & Simone Scotti & Chao Zhou, 2021. "The Alpha‐Heston stochastic volatility model," Mathematical Finance, Wiley Blackwell, vol. 31(3), pages 943-978, July.
    20. Barczy, Mátyás & Basrak, Bojan & Kevei, Péter & Pap, Gyula & Planinić, Hrvoje, 2021. "Statistical inference of subcritical strongly stationary Galton–Watson processes with regularly varying immigration," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 33-75.

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