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Error analysis of the optimal quantization algorithm for obstacle problems

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  • Bally, Vlad
  • Pagès, Gilles

Abstract

In the paper Bally and Pagès (2000) an algorithm based on an optimal discrete quantization tree is designed to compute the solution of multi-dimensional obstacle problems for homogeneous -valued Markov chains (Xk)0[less-than-or-equals, slant]k[less-than-or-equals, slant]n. This tree is made up with the (optimal) quantization grids of every Xk. Then a dynamic programming formula is naturally designed on it. The pricing of multi-asset American style vanilla options is a typical example of such problems. The first part of this paper is devoted to the analysis of the Lp-error induced by the quantization procedure. A second part deals with the analysis of the statistical error induced by the Monte Carlo estimation of the transition weights of the quantization tree.

Suggested Citation

  • Bally, Vlad & Pagès, Gilles, 2003. "Error analysis of the optimal quantization algorithm for obstacle problems," Stochastic Processes and their Applications, Elsevier, vol. 106(1), pages 1-40, July.
    • Handle: RePEc:eee:spapps:v:106:y:2003:i:1:p:1-40
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    References listed on IDEAS

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    1. BALLY Vlad & PAGÈS Gilles & PRINTEMS Jacques, 2001. "A stochastic quantization method for nonlinear problems," Monte Carlo Methods and Applications, De Gruyter, vol. 7(1-2), pages 21-34, December.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    3. N. El Karoui & S. Peng & M. C. Quenez, 1997. "Backward Stochastic Differential Equations in Finance," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 1-71, January.
    4. Philip Protter & Emmanuelle Clément & Damien Lamberton, 2002. "An analysis of a least squares regression method for American option pricing," Finance and Stochastics, Springer, vol. 6(4), pages 449-471.
    5. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    6. Pagès Gilles & Printems Jacques, 2003. "Optimal quadratic quantization for numerics: the Gaussian case," Monte Carlo Methods and Applications, De Gruyter, vol. 9(2), pages 135-165, April.
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    Citations

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    Cited by:

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    2. Gobet, Emmanuel & Makhlouf, Azmi, 2010. "-time regularity of BSDEs with irregular terminal functions," Stochastic Processes and their Applications, Elsevier, vol. 120(7), pages 1105-1132, July.
    3. Bruno Bouchard & Jean-François Chassagneux & Géraldine Bouveret, 2016. "A backward dual representation for the quantile hedging of Bermudan options," Post-Print hal-01069270, HAL.
    4. Jean-Franc{c}ois Chassagneux & Junchao Chen & Noufel Frikha, 2022. "Deep Runge-Kutta schemes for BSDEs," Papers 2212.14372, arXiv.org.
    5. Marie Bernhart & Huyên Pham & Peter Tankov & Xavier Warin, 2011. "Swing Options Valuation:a BSDE with Constrained Jumps Approach," Working Papers hal-00553356, HAL.
    6. Jean-Franc{c}ois Chassagneux & Mohan Yang, 2021. "Numerical approximation of singular Forward-Backward SDEs," Papers 2106.15496, arXiv.org.
    7. Crisan, D. & Manolarakis, K. & Touzi, N., 2010. "On the Monte Carlo simulation of BSDEs: An improvement on the Malliavin weights," Stochastic Processes and their Applications, Elsevier, vol. 120(7), pages 1133-1158, July.
    8. Jin, Xing & Li, Xun & Tan, Hwee Huat & Wu, Zhenyu, 2013. "A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction," European Journal of Operational Research, Elsevier, vol. 231(2), pages 362-370.
    9. Olivier Aj Bardou & Sandrine Bouthemy & Gilles Pag`es, 2007. "Optimal quantization for the pricing of swing options," Papers 0705.2110, arXiv.org.
    10. Céline Labart & Jérôme Lelong, 2011. "A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options," Working Papers hal-00567729, HAL.
    11. Sebastian Becker & Patrick Cheridito & Arnulf Jentzen & Timo Welti, 2019. "Solving high-dimensional optimal stopping problems using deep learning," Papers 1908.01602, arXiv.org, revised Aug 2021.
    12. Jean-Franc{c}ois Chassagneux & Junchao Chen & Noufel Frikha & Chao Zhou, 2021. "A learning scheme by sparse grids and Picard approximations for semilinear parabolic PDEs," Papers 2102.12051, arXiv.org.
    13. Said Hamadène & Monique Jeanblanc, 2007. "On the Starting and Stopping Problem: Application in Reversible Investments," Mathematics of Operations Research, INFORMS, vol. 32(1), pages 182-192, February.
    14. Pagès, Gilles & Sagna, Abass, 2018. "Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering," Stochastic Processes and their Applications, Elsevier, vol. 128(3), pages 847-883.
    15. J. Bonnans & Zhihao Cen & Thibault Christel, 2012. "Energy contracts management by stochastic programming techniques," Annals of Operations Research, Springer, vol. 200(1), pages 199-222, November.
    16. Gassiat, Paul & Kharroubi, Idris & Pham, Huyên, 2012. "Time discretization and quantization methods for optimal multiple switching problem," Stochastic Processes and their Applications, Elsevier, vol. 122(5), pages 2019-2052.
    17. Labart Céline & Lelong Jérôme, 2013. "A parallel algorithm for solving BSDEs," Monte Carlo Methods and Applications, De Gruyter, vol. 19(1), pages 11-39, March.

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