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Exact Simulation of Jump-Diffusion Processes with Monte Carlo Applications

Author

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  • Casella, Bruno
  • Roberts, Gareth O.

Abstract

We introduce a novel algorithm (JEA) to simulate exactly from a class of one-dimensional jump-diffusion processes with state-dependent intensity. The simulation of the continuous component builds on the recent Exact Algorithm (Beskos et al., Bernoulli 12(6):1077–1098, 2006a). The simulation of the jump component instead employs a thinning algorithm with stochastic acceptance probabilities in the spirit of Glasserman and Merener (Proc R Soc Lond Ser A Math Phys Eng Sci 460(2041):111–127, 2004). In turn JEA allows unbiased Monte Carlo simulation of a wide class of functionals of the process’ trajectory, including discrete averages, max/min, crossing events, hitting times. Our numerical experiments show that the method outperforms Monte Carlo methods based on the Euler discretization.

Suggested Citation

  • Casella, Bruno & Roberts, Gareth O., 2011. "Exact Simulation of Jump-Diffusion Processes with Monte Carlo Applications," MPRA Paper 95217, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:95217
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Flávio B. Gonçalves & Gareth O. Roberts, 2014. "Exact Simulation Problems for Jump-Diffusions," Methodology and Computing in Applied Probability, Springer, vol. 16(4), pages 907-930, December.
    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. Wanmo Kang & Jong Mun Lee, 2019. "Unbiased Sensitivity Estimation of One-Dimensional Diffusion Processes," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 334-353, February.
    4. Shi, Menghuan & Huang, Yuyang & Li, Xiaofei & Lei, Yaliu & Du, Junjie, 2025. "Monte Carlo method in stock trading research based on accelerated diffusion theory with jumps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 672(C).
    5. Hermann, Simone & Ickstadt, Katja & Müller, Christine H., 2018. "Bayesian prediction for a jump diffusion process – With application to crack growth in fatigue experiments," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 83-96.
    6. Bikourne, Mariem & Akdim, Khadija & Ez-Zetouni, Adil, 2025. "Stochastic analysis of an economic growth model incorporating Itô–Lévy driven investment, optimal control and numerical simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    7. Yehuda Arav & Eyal Fattal & Ziv Klausner, 2022. "Is the Increased Transmissibility of SARS-CoV-2 Variants Driven by within or Outside-Host Processes?," Mathematics, MDPI, vol. 10(19), pages 1-17, September.
    8. Murray Pollock, 2015. "On the Exact Simulation of (Jump) Diffusion Bridges," Papers 1505.03030, arXiv.org.
    9. K. Giesecke & H. Kakavand & M. Mousavi, 2011. "Exact Simulation of Point Processes with Stochastic Intensities," Operations Research, INFORMS, vol. 59(5), pages 1233-1245, October.
    10. Herrmann, Samuel & Massin, Nicolas, 2023. "Exact simulation of the first passage time through a given level of jump diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 553-576.
    11. Kay Giesecke & Dmitry Smelov, 2013. "Exact Sampling of Jump Diffusions," Operations Research, INFORMS, vol. 61(4), pages 894-907, August.
    12. Rudiger Frey & Verena Kock, 2021. "Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics," Papers 2109.11403, arXiv.org, revised Sep 2021.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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