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Estimation for the change point of the volatility in a stochastic differential equation

Author

Listed:
  • Stefano Iacus

    (Department of Economics, Business and Statistics, University of Milan, IT)

  • Nakahiro Yoshida

    (Graduate School of Mathematical Sciences, Tokyo University, Tokyo)

Abstract

We consider a multidimensional Ito process Y=(Y_t), t in [0,T], with some unknown drift coefficient process b_t and volatility coefficient sigma(X_t,theta) with covariate process X=(X_t), t in[0,T], the function sigma(x,theta) being known up to theta in Theta. For this model we consider a change point problem for the parameter theta in the volatility component. The change is supposed to occur at some point t* in (0,T). Given discrete time observations from the process (X,Y), we propose quasi-maximum likelihood estimation of the change point. We present the rate of convergence of the change point estimator and the limit thereoms of aymptotically mixed type.

Suggested Citation

  • Stefano Iacus & Nakahiro Yoshida, 2009. "Estimation for the change point of the volatility in a stochastic differential equation," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1084, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1084
    Note: oai:cdlib1:unimi-1084
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    References listed on IDEAS

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    1. Stefano M. Iacus & Nakahiro Yoshida, 2010. "Numerical Analysis of Volatility Change Point Estimators for Discretely Sampled Stochastic Differential Equations," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(1‐2), pages 107-127, February.
    2. Jushan Bai, 1994. "Least Squares Estimation Of A Shift In Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 453-472, September.
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    4. Sangyeol Lee & Yoichi Nishiyama & Nakahiro Yoshida, 2006. "Test for Parameter Change in Diffusion Processes by Cusum Statistics Based on One-step Estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 211-222, June.
    5. Shixin, Gan, 1997. "The Hájek-Rényi inequality for Banach space valued martingales and the p smoothness of Banach spaces," Statistics & Probability Letters, Elsevier, vol. 32(3), pages 245-248, March.
    6. Sangyeol Lee & Jeongcheol Ha & Okyoung Na & Seongryong Na, 2003. "The Cusum Test for Parameter Change in Time Series Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(4), pages 781-796, December.
    7. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    8. Junmo Song & Sangyeol Lee, 2009. "Test for parameter change in discretely observed diffusion processes," Statistical Inference for Stochastic Processes, Springer, vol. 12(2), pages 165-183, June.
    9. Chen, Gongmeng & Choi, Yoon K. & Zhou, Yong, 2005. "Nonparametric estimation of structural change points in volatility models for time series," Journal of Econometrics, Elsevier, vol. 126(1), pages 79-114, May.
    10. repec:bla:ecnote:v:39:y:2010:i:s1:p:107-127 is not listed on IDEAS
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    Cited by:

    1. Yozo Tonaki & Yusuke Kaino & Masayuki Uchida, 2023. "Estimation for change point of discretely observed ergodic diffusion processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 142-183, March.
    2. Tonaki, Yozo & Uchida, Masayuki, 2023. "Change point inference in ergodic diffusion processes based on high frequency data," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 1-39.
    3. Bibinger, Markus & Jirak, Moritz & Vetter, Mathias, 2015. "Nonparametric change-point analysis of volatility," SFB 649 Discussion Papers 2015-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    5. Stefano M. Iacus & Nakahiro Yoshida, 2010. "Numerical Analysis of Volatility Change Point Estimators for Discretely Sampled Stochastic Differential Equations," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(1‐2), pages 107-127, February.
    6. Alessandra Micheletti & Giacomo Aletti & Giulia Ferrandi & Danilo Bertoni & Daniele Cavicchioli & Roberto Pretolani, 2020. "A weighted $$\chi ^2$$ χ 2 test to detect the presence of a major change point in non-stationary Markov chains," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 899-912, December.
    7. A. Gregorio & S. M. Iacus, 2019. "Empirical $$L^2$$ L 2 -distance test statistics for ergodic diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 22(2), pages 233-261, July.
    8. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.
    9. Markus Bibinger & Moritz Jirak & Mathias Vetter, 2015. "Nonparametric change-point analysis of volatility," SFB 649 Discussion Papers SFB649DP2015-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Yozo Tonaki & Yusuke Kaino & Masayuki Uchida, 2022. "Adaptive tests for parameter changes in ergodic diffusion processes from discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 397-430, July.

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