IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v21y2015i1p1-32n4.html
   My bibliography  Save this article

Functional quantization-based stratified sampling methods

Author

Listed:
  • Corlay Sylvain

    (Bloomberg L.P. Quantitative Research, 731 Lexington avenue, New York, NY 10022, USA)

  • Pagès Gilles

    (Laboratoire de Probabilités et Modèles Aléatoires, UMR 7599, Université Paris 6, case 188, 4, pl. Jussieu, F-75252 Paris Cedex 5, France)

Abstract

In this article, we propose several quantization-based stratified sampling methods to reduce the variance of a Monte Carlo simulation. Theoretical aspects of stratification lead to a strong link between optimal quadratic quantization and the variance reduction that can be achieved with stratified sampling. We first put the emphasis on the consistency of quantization for partitioning the state space in stratified sampling methods in both finite and infinite-dimensional cases. We show that the proposed quantization-based strata design has uniform efficiency among the class of Lipschitz continuous functionals. Then a stratified sampling algorithm based on product functional quantization is proposed for path-dependent functionals of multi-factor diffusions. The method is also available for other Gaussian processes such as Brownian bridge or Ornstein–Uhlenbeck processes. We derive in detail the case of Ornstein–Uhlenbeck processes. We also study the balance between the algorithmic complexity of the simulation and the variance reduction factor.

Suggested Citation

  • Corlay Sylvain & Pagès Gilles, 2015. "Functional quantization-based stratified sampling methods," Monte Carlo Methods and Applications, De Gruyter, vol. 21(1), pages 1-32, March.
    • Handle: RePEc:bpj:mcmeap:v:21:y:2015:i:1:p:1-32:n:4
    DOI: 10.1515/mcma-2014-0010
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2014-0010
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2014-0010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luschgy, Harald & Pagès, Gilles, 2006. "Functional quantization of a class of Brownian diffusions: A constructive approach," Stochastic Processes and their Applications, Elsevier, vol. 116(2), pages 310-336, February.
    2. Sabelfeld, K.K. & Mozartova, N.S., 2011. "Sparsified Randomization algorithms for low rank approximations and applications to integral equations and inhomogeneous random field simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(2), pages 295-317.
    3. Pierre Etore & Gersende Fort & Benjamin Jourdain & Eric Moulines, 2011. "On adaptive stratification," Annals of Operations Research, Springer, vol. 189(1), pages 127-154, September.
    4. Benjamin Jourdain & Bernard Lapeyre & Piergiacomo Sabino, 2011. "Convenient Multiple Directions of Stratification," Post-Print hal-00477403, HAL.
    5. Benjamin Jourdain & Bernard Lapeyre & Piergiacomo Sabino, 2011. "Convenient Multiple Directions Of Stratification," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 867-897.
    6. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    7. Vlad Bally & Gilles Pagès & Jacques Printems, 2005. "A Quantization Tree Method For Pricing And Hedging Multidimensional American Options," Mathematical Finance, Wiley Blackwell, vol. 15(1), pages 119-168, January.
    8. Olivier Bardou & Sandrine Bouthemy & Gilles Pages, 2009. "Optimal Quantization for the Pricing of Swing Options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(2), pages 183-217.
    9. Pierre Étoré & Benjamin Jourdain, 2010. "Adaptive Optimal Allocation in Stratified Sampling Methods," Methodology and Computing in Applied Probability, Springer, vol. 12(3), pages 335-360, September.
    10. Mark Broadie & Paul Glasserman & Steven Kou, 1997. "A Continuity Correction for Discrete Barrier Options," Mathematical Finance, Wiley Blackwell, vol. 7(4), pages 325-349, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anne Laure Bronstein & Gilles Pagès & Jacques Portès, 2013. "Multi-asset American Options and Parallel Quantization," Methodology and Computing in Applied Probability, Springer, vol. 15(3), pages 547-561, September.
    2. Gilles Pag`es & Benedikt Wilbertz, 2011. "GPGPUs in computational finance: Massive parallel computing for American style options," Papers 1101.3228, arXiv.org.
    3. Sayah, Toni, 2019. "Adaptive stratified Monte Carlo algorithm for numerical computation of integrals," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 157(C), pages 143-158.
    4. Carl Chiarella & Les Clewlow & Boda Kang, 2016. "The Evaluation Of Multiple Year Gas Sales Agreement With Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-25, February.
    5. Francisco Bernal & Emmanuel Gobet & Jacques Printems, 2020. "Volatility Uncertainty Quantification in a Stochastic Control Problem Applied to Energy," Methodology and Computing in Applied Probability, Springer, vol. 22(1), pages 135-159, March.
    6. Frikha Noufel & Sagna Abass, 2012. "Quantization based recursive importance sampling," Monte Carlo Methods and Applications, De Gruyter, vol. 18(4), pages 287-326, December.
    7. Gilles Pag`es & Christian Yeo, 2024. "Convex ordering for stochastic control: the swing contracts case," Papers 2406.07464, arXiv.org, revised Jun 2024.
    8. Ning Cai & Chenxu Li & Chao Shi, 2014. "Closed-Form Expansions of Discretely Monitored Asian Options in Diffusion Models," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 789-822, August.
    9. Sagna, Abass, 2011. "Pricing of barrier options by marginal functional quantization," Monte Carlo Methods and Applications, De Gruyter, vol. 17(4), pages 371-398, December.
    10. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    11. Unterschultz, James R., 2000. "New Instruments For Co-Ordination And Risk Sharing Within The Canadian Beef Industry," Project Report Series 24046, University of Alberta, Department of Resource Economics and Environmental Sociology.
    12. Chiarella, Carl & Kang, Boda & Nikitopoulos, Christina Sklibosios & Tô, Thuy-Duong, 2013. "Humps in the volatility structure of the crude oil futures market: New evidence," Energy Economics, Elsevier, vol. 40(C), pages 989-1000.
    13. Chuong Luong & Nikolai Dokuchaev, 2016. "Modeling Dependency Of Volatility On Sampling Frequency Via Delay Equations," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-21, June.
    14. Dong, Wenfeng & Kang, Boda, 2019. "Analysis of a multiple year gas sales agreement with make-up, carry-forward and indexation," Energy Economics, Elsevier, vol. 79(C), pages 76-96.
    15. Alain Monfort & Olivier Féron, 2012. "Joint econometric modeling of spot electricity prices, forwards and options," Review of Derivatives Research, Springer, vol. 15(3), pages 217-256, October.
    16. Hilscher, Jens & Raviv, Alon, 2014. "Bank stability and market discipline: The effect of contingent capital on risk taking and default probability," Journal of Corporate Finance, Elsevier, vol. 29(C), pages 542-560.
    17. Luis M. Abadie, 2009. "Valuation of Long-Term Investments in Energy Assets under Uncertainty," Energies, MDPI, vol. 2(3), pages 1-31, September.
    18. Guedes, José & Santos, Pedro, 2016. "Valuing an offshore oil exploration and production project through real options analysis," Energy Economics, Elsevier, vol. 60(C), pages 377-386.
    19. Delphine Lautier & Franck Raynaud, 2012. "Systemic Risk in Energy Derivative Markets: A Graph-Theory Analysis," The Energy Journal, , vol. 33(3), pages 215-240, July.
    20. Bühler, Wolfgang & Korn, Olaf, 1998. "Hedging langfristiger Lieferverpflichtungen mit kurzfristigen Futures: möglich oder unmöglich?," ZEW Discussion Papers 98-20, ZEW - Leibniz Centre for European Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:mcmeap:v:21:y:2015:i:1:p:1-32:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.