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Variance reduction methods for simulation of densities on Wiener space

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  • Arturo Kohatsu
  • Roger Pettersson

Abstract

We develop a general error analysis framework for the Monte Carlo simulation of densities for functionals in Wiener space. We also study variance reduction methods with the help of Malliavin derivatives. For this, we give some general heuristic principles which are applied to diffusion processes. A comparison with kernel density estimates is made.

Suggested Citation

  • 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.
    • Handle: RePEc:upf:upfgen:597
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    References listed on IDEAS

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    1. Eric Fournié & Jean-Michel Lasry & Pierre-Louis Lions & Jérôme Lebuchoux & Nizar Touzi, 1999. "Applications of Malliavin calculus to Monte Carlo methods in finance," Finance and Stochastics, Springer, vol. 3(4), pages 391-412.
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    Cited by:

    1. Abbas-Turki Lokman A. & Bouselmi Aych I. & Mikou Mohammed A., 2014. "Toward a coherent Monte Carlo simulation of CVA," Monte Carlo Methods and Applications, De Gruyter, vol. 20(3), pages 195-216, September.
    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. Bouleau Nicolas, 2005. "Dirichlet Forms in Simulation," Monte Carlo Methods and Applications, De Gruyter, vol. 11(4), pages 385-395, December.
    4. Gibson, Rajna & Lhabitant, Francois-Serge & Talay, Denis, 2010. "Modeling the Term Structure of Interest Rates: A Review of the Literature," Foundations and Trends(R) in Finance, now publishers, vol. 5(1–2), pages 1-156, December.
    5. Gobet, Emmanuel & Labart, Céline, 2007. "Error expansion for the discretization of backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 117(7), pages 803-829, July.
    6. Moez Mrad & Nizar Touzi & Amina Zeghal, 2006. "Monte Carlo Estimation of a Joint Density Using Malliavin Calculus, and Application to American Options," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 497-531, June.
    7. Guyon, Julien, 2006. "Euler scheme and tempered distributions," Stochastic Processes and their Applications, Elsevier, vol. 116(6), pages 877-904, June.
    8. Talay, Denis & Zheng, Ziyu, 2004. "Approximation of quantiles of components of diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 109(1), pages 23-46, January.
    9. Arturo Kohatsu & Montero Miquel, 2003. "Malliavin calculus in finance," Economics Working Papers 672, Department of Economics and Business, Universitat Pompeu Fabra.
    10. Kebaier, Ahmed & Kohatsu-Higa, Arturo, 2008. "An optimal control variance reduction method for density estimation," Stochastic Processes and their Applications, Elsevier, vol. 118(12), pages 2143-2180, December.
    11. Nicola Cufaro Petroni & Piergiacomo Sabino, 2013. "Multidimensional quasi-Monte Carlo Malliavin Greeks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 36(2), pages 199-224, November.
    12. Bouchard, Bruno & Touzi, Nizar, 2004. "Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 111(2), pages 175-206, June.
    13. Hans‐Peter Bermin & Arturo Kohatsu‐Higa & Miquel Montero, 2003. "Local Vega Index and Variance Reduction Methods," Mathematical Finance, Wiley Blackwell, vol. 13(1), pages 85-97, January.

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    More about this item

    Keywords

    Stochastic differential equations; weak approximation; variance reduction; kernel density estimation;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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