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Euler scheme and tempered distributions

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  • Guyon, Julien

Abstract

Given a smooth -valued diffusion starting at point x, we study how fast the Euler scheme with time step 1/n converges in law to the random variable . To be precise, we look for the class of test functions f for which the approximate expectation converges with speed 1/n to . When f is smooth with polynomially growing derivatives or, under a uniform hypoellipticity condition for X, when f is only measurable and bounded, it is known that there exists a constant C1f(x) such that If X is uniformly elliptic, we expand this result to the case when f is a tempered distribution. In such a case, (resp. ) has to be understood as (resp. ) where p(t,x,[dot operator]) (resp. pn(t,x,[dot operator])) is the density of (resp. ). In particular, (1) is valid when f is a measurable function with polynomial growth, a Dirac mass or any derivative of a Dirac mass. We even show that (1) remains valid when f is a measurable function with exponential growth. Actually our results are symmetric in the two space variables x and y of the transition density and we prove that for a function and an O(1/n2) remainder rn which are shown to have gaussian tails and whose dependence on t is made precise. We give applications to option pricing and hedging, proving numerical convergence rates for prices, deltas and gammas.

Suggested Citation

  • Guyon, Julien, 2006. "Euler scheme and tempered distributions," Stochastic Processes and their Applications, Elsevier, vol. 116(6), pages 877-904, June.
    • Handle: RePEc:eee:spapps:v:116:y:2006:i:6:p:877-904
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    References listed on IDEAS

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    1. Arturo Kohatsu & Roger Pettersson, 2002. "Variance reduction methods for simulation of densities on Wiener space," Economics Working Papers 597, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Konakov Valentin & Mammen Enno, 2002. "Edgeworth type expansions for Euler schemes for stochastic differential equations," Monte Carlo Methods and Applications, De Gruyter, vol. 8(3), pages 271-286, December.
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    Cited by:

    1. Aurélien Alfonsi & Benjamin Jourdain & Arturo Kohatsu-Higa, 2014. "Pathwise optimal transport bounds between a one-dimensional diffusion and its Euler scheme," Post-Print hal-00727430, HAL.
    2. Pagès Gilles, 2007. "Multi-step Richardson-Romberg Extrapolation: Remarks on Variance Control and Complexity," Monte Carlo Methods and Applications, De Gruyter, vol. 13(1), pages 37-70, April.
    3. Okano Yusuke & Yamada Toshihiro, 2019. "A control variate method for weak approximation of SDEs via discretization of numerical error of asymptotic expansion," Monte Carlo Methods and Applications, De Gruyter, vol. 25(3), pages 239-252, September.
    4. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1, July-Dece.
    5. Rainer Avikainen, 2009. "On irregular functionals of SDEs and the Euler scheme," Finance and Stochastics, Springer, vol. 13(3), pages 381-401, September.
    6. Dan Pirjol & Lingjiong Zhu, 2020. "Asymptotics of the time-discretized log-normal SABR model: The implied volatility surface," Papers 2001.09850, arXiv.org, revised Mar 2020.
    7. Nicola Bruti-Liberati & Eckhard Platen, 2007. "Approximation of jump diffusions in finance and economics," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 283-312, May.
    8. Rey Clément, 2017. "Convergence in total variation distance of a third order scheme for one-dimensional diffusion processes," Monte Carlo Methods and Applications, De Gruyter, vol. 23(1), pages 1-12, March.
    9. Taguchi, Dai & Tanaka, Akihiro, 2020. "Probability density function of SDEs with unbounded and path-dependent drift coefficient," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5243-5289.
    10. Benjamin Jourdain & Mohamed Sbai, 2013. "High order discretization schemes for stochastic volatility models," Post-Print hal-00409861, HAL.
    11. Valentin Konakov & Stéphane Menozzi, 2011. "Weak Error for Stable Driven Stochastic Differential Equations: Expansion of the Densities," Journal of Theoretical Probability, Springer, vol. 24(2), pages 454-478, June.
    12. Cattiaux Patrick & León José R. & Prieur Clémentine, 2017. "Invariant density estimation for a reflected diffusion using an Euler scheme," Monte Carlo Methods and Applications, De Gruyter, vol. 23(2), pages 71-88, June.
    13. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2007, January-A.
    14. Rey, Clément, 2019. "Approximation of Markov semigroups in total variation distance under an irregular setting: An application to the CIR process," Stochastic Processes and their Applications, Elsevier, vol. 129(2), pages 539-571.
    15. repec:hal:wpaper:hal-00727430 is not listed on IDEAS
    16. Dan Pirjol & Lingjiong Zhu, 2017. "Asymptotics for the Euler-Discretized Hull-White Stochastic Volatility Model," Papers 1707.00899, arXiv.org.
    17. Dan Pirjol & Lingjiong Zhu, 2018. "Asymptotics for the Euler-Discretized Hull-White Stochastic Volatility Model," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 289-331, March.

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