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On the Approximate Maximum Likelihood Estimation for Diffusion Processes

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  • Chang, Jinyuan
  • Chen, Songxi

Abstract

The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. Aït-Sahalia [J. Finance 54 (1999) 1361–1395; Econometrica 70 (2002) 223–262] proposed asymptotic expansions to the transition densities of diffusion processes, which lead to an approximate maximum likelihood estimation (AMLE) for parameters. Built on Aït-Sahalia’s [Econometrica 70 (2002) 223–262; Ann. Statist. 36 (2008) 906–937] proposal and analysis on the AMLE, we establish the consistency and convergence rate of the AMLE, which reveal the roles played by the number of terms used in the asymptotic density expansions and the sampling interval between successive observations. We find conditions under which the AMLE has the same asymptotic distribution as that of the full MLE. A first order approximation to the Fisher information matrix is proposed.

Suggested Citation

  • Chang, Jinyuan & Chen, Songxi, 2011. "On the Approximate Maximum Likelihood Estimation for Diffusion Processes," MPRA Paper 46279, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:46279
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    References listed on IDEAS

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    Cited by:

    1. Wang, Bin & Zheng, Xu, 2022. "Testing for the presence of jump components in jump diffusion models," Journal of Econometrics, Elsevier, vol. 230(2), pages 483-509.
    2. Yang, Nian & Chen, Nan & Wan, Xiangwei, 2019. "A new delta expansion for multivariate diffusions via the Itô-Taylor expansion," Journal of Econometrics, Elsevier, vol. 209(2), pages 256-288.
    3. Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    4. Choi, Seungmoon, 2018. "Comparison of the Korean and US Stock Markets Using Continuous-time Stochastic Volatility Models," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 40(4), pages 1-22.
    5. Park, Joon Y. & Wang, Bin, 2021. "Nonparametric estimation of jump diffusion models," Journal of Econometrics, Elsevier, vol. 222(1), pages 688-715.
    6. Choi, Hwan-sik & Jeong, Minsoo & Park, Joon Y., 2014. "An asymptotic analysis of likelihood-based diffusion model selection using high frequency data," Journal of Econometrics, Elsevier, vol. 178(P3), pages 539-557.
    7. Francesco Campigli & Gabriele Tedeschi & Maria Cristina Recchioni, 2021. "The talkative variables of the hybrid Heston model: Yields’ maturity and economic (in)stability," Working Papers 2021/03, Economics Department, Universitat Jaume I, Castellón (Spain).
    8. Choi, Hwan-sik, 2016. "Information theory for maximum likelihood estimation of diffusion models," Journal of Econometrics, Elsevier, vol. 191(1), pages 110-128.
    9. Tao Zou & Song Xi Chen, 2017. "Enhancing Estimation for Interest Rate Diffusion Models With Bond Prices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 486-498, July.
    10. Li, Chenxu & Chen, Dachuan, 2016. "Estimating jump–diffusions using closed-form likelihood expansions," Journal of Econometrics, Elsevier, vol. 195(1), pages 51-70.
    11. Lee, Yoon Dong & Song, Seongjoo & Lee, Eun-Kyung, 2014. "The delta expansion for the transition density of diffusion models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 694-705.
    12. Choi, Seungmoon, 2015. "Explicit form of approximate transition probability density functions of diffusion processes," Journal of Econometrics, Elsevier, vol. 187(1), pages 57-73.
    13. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    14. Kevin W. Lu & Phillip J. Paine & Simon P. Preston & Andrew T. A. Wood, 2022. "Approximate maximum likelihood estimation for one‐dimensional diffusions observed on a fine grid," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1085-1114, September.
    15. Eva María Ramos-Ábalos & Ramón Gutiérrez-Sánchez & Ahmed Nafidi, 2020. "Powers of the Stochastic Gompertz and Lognormal Diffusion Processes, Statistical Inference and Simulation," Mathematics, MDPI, vol. 8(4), pages 1-13, April.
    16. Ruijun Bu & Jihyun Kim & Bin Wang, 2020. "Uniform and Lp Convergences of Nonparametric Estimation for Diffusion Models," Working Papers 202021, University of Liverpool, Department of Economics.
    17. Kim, Jihyun & Park, Joon & Wang, Bin, 2020. "Estimation of Volatility Functions in Jump Diffusions Using Truncated Bipower Increments," TSE Working Papers 20-1096, Toulouse School of Economics (TSE).

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    More about this item

    Keywords

    Asymptotic expansion; Asymptotic normality; Consistency; Discrete time observation; Maximum likelihood estimation.;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • G0 - Financial Economics - - General

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