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Estimating jump–diffusions using closed-form likelihood expansions

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  • Li, Chenxu
  • Chen, Dachuan

Abstract

The indispensable role of likelihood expansions in financial econometrics for continuous-time models has been established since the ground-breaking work of Aït-Sahalia (1999, 2002a, 2008). Jump–diffusions play an important role in modeling a variety of economic and financial variables. As a significant generalization of Li (2013), we propose a new closed-form expansion for transition density of Poisson-driven jump–diffusion models and its application in maximum-likelihood estimation based on discretely sampled data. Technically speaking, our expansion is obtained by perturbing paths of the underlying model; correction terms can be calculated explicitly using any symbolic software. Numerical examples and Monte Carlo evidence for illustrating the performance of density expansion and the resulting approximate MLE are provided in order to demonstrate the practical applicability of the method. Using the theoretical results in Hayashi and Ishikawa (2012), some convergence properties related to the density expansion and the approximate MLE method can be justified under some standard sufficient (but not necessary) conditions.

Suggested Citation

  • Li, Chenxu & Chen, Dachuan, 2016. "Estimating jump–diffusions using closed-form likelihood expansions," Journal of Econometrics, Elsevier, vol. 195(1), pages 51-70.
  • Handle: RePEc:eee:econom:v:195:y:2016:i:1:p:51-70
    DOI: 10.1016/j.jeconom.2016.07.001
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    Cited by:

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    2. Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    3. Kenichiro Shiraya & Akihiko Takahashi, 2019. "Pricing Average and Spread Options Under Local-Stochastic Volatility Jump-Diffusion Models," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 303-333, February.
    4. Wan, Xiangwei & Yang, Nian, 2021. "Hermite expansion of transition densities and European option prices for multivariate diffusions with jumps," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    5. Jakobsen, Nina Munkholt & Sørensen, Michael, 2019. "Estimating functions for jump–diffusions," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3282-3318.
    6. 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.
    7. Fan Jiang & Xin Zang & Jingping Yang, 2020. "Asymptotic expansion for the transition densities of stochastic differential equations driven by the gamma processes," Papers 2003.06218, arXiv.org.
    8. Chiara Amorino & Arnaud Gloter, 2021. "Joint estimation for volatility and drift parameters of ergodic jump diffusion processes via contrast function," Statistical Inference for Stochastic Processes, Springer, vol. 24(1), pages 61-148, April.

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    More about this item

    Keywords

    Maximum-likelihood estimation; Jump–diffusion; Discrete observations; Transition density; Expansion;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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