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On arbitrarily slow convergence rates for strong numerical approximations of Cox–Ingersoll–Ross processes and squared Bessel processes

Author

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  • Mario Hefter

    (Universität Kaiserslautern)

  • Arnulf Jentzen

    (Eidgenössische Technische Hochschule Zürich)

Abstract

Cox–Ingersoll–Ross (CIR) processes are extensively used in state-of-the-art models for the pricing of financial derivatives. The prices of financial derivatives are very often approximately computed by means of explicit or implicit Euler- or Milstein-type discretization methods based on equidistant evaluations of the driving noise processes. In this article, we study the strong convergence speeds of all such discretization methods. More specifically, the main result of this article reveals that each such discretization method achieves at most a strong convergence order of δ / 2 $\delta /2$ , where 0

Suggested Citation

  • Mario Hefter & Arnulf Jentzen, 2019. "On arbitrarily slow convergence rates for strong numerical approximations of Cox–Ingersoll–Ross processes and squared Bessel processes," Finance and Stochastics, Springer, vol. 23(1), pages 139-172, January.
    • Handle: RePEc:spr:finsto:v:23:y:2019:i:1:d:10.1007_s00780-018-0375-5
    DOI: 10.1007/s00780-018-0375-5
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    References listed on IDEAS

    as
    1. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    2. Jean-Francois Chassagneux & Antoine Jacquier & Ivo Mihaylov, 2014. "An explicit Euler scheme with strong rate of convergence for financial SDEs with non-Lipschitz coefficients," Papers 1405.3561, arXiv.org, revised Apr 2016.
    3. Griselda Deelstra & Freddy Delbaen, 1998. "Convergence of discretised stochastic interest rate: processes with stochastic drift term," ULB Institutional Repository 2013/7584, ULB -- Universite Libre de Bruxelles.
    4. G. Deelstra & F. Delbaen, 1998. "Convergence of discretized stochastic (interest rate) processes with stochastic drift term," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 14(1), pages 77-84, March.
    5. Alfonsi, Aurélien, 2013. "Strong order one convergence of a drift implicit Euler scheme: Application to the CIR process," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 602-607.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Yunyu Zhang, 2020. "The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-13, February.
    2. Simon J. A. Malham & Jiaqi Shen & Anke Wiese, 2020. "Series expansions and direct inversion for the Heston model," Papers 2008.08576, arXiv.org, revised Jan 2021.
    3. C'onall Kelly & Gabriel J. Lord, 2021. "An adaptive splitting method for the Cox-Ingersoll-Ross process," Papers 2112.09465, arXiv.org, revised Feb 2023.
    4. Madalina Deaconu & Samuel Herrmann, 2023. "Strong Approximation of Bessel Processes," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-24, March.

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    More about this item

    Keywords

    Cox–Ingersoll–Ross process; Squared Bessel process; Stochastic differential equation; Strong (pathwise) approximation; Lower error bound; Optimal approximation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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