IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v46y2000i10p1349-1364.html
   My bibliography  Save this article

Variance Reduction Techniques for Estimating Value-at-Risk

Author

Listed:
  • Paul Glasserman

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Philip Heidelberger

    (IBM Research Division, T.J. Watson Research Center, Yorktown Heights, New York, 10598)

  • Perwez Shahabuddin

    (IEOR Department, Columbia University, New York, New York 10027)

Abstract

This paper describes, analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation.Obtaining accurate estimates of such loss probabilities is essential to calculating value-at-risk, which is a quantile of the loss distribution. The method employs a quadratic ("delta-gamma") approximation to the change in portfolio value to guide the selection of effective variance reduction techniques;specifically importance sampling and stratified sampling.If the approximation is exact, then the importance sampling is shown to be asymptotically optimal.Numerical results indicate that an appropriate combination of importance sampling and stratified sampling can result in large variance reductions when estimating the probability of large portfolio losses.

Suggested Citation

  • Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 2000. "Variance Reduction Techniques for Estimating Value-at-Risk," Management Science, INFORMS, vol. 46(10), pages 1349-1364, October.
    • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:10:p:1349-1364
    DOI: 10.1287/mnsc.46.10.1349.12274
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.46.10.1349.12274
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.46.10.1349.12274?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
    ---><---

    References listed on IDEAS

    as
    1. Perwez Shahabuddin, 1994. "Importance Sampling for the Simulation of Highly Reliable Markovian Systems," Management Science, INFORMS, vol. 40(3), pages 333-352, March.
    2. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 1999. "Asymptotically Optimal Importance Sampling and Stratification for Pricing Path‐Dependent Options," Mathematical Finance, Wiley Blackwell, vol. 9(2), pages 117-152, April.
    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. Hernan P. Awad & Peter W. Glynn & Reuven Y. Rubinstein, 2013. "Zero-Variance Importance Sampling Estimators for Markov Process Expectations," Mathematics of Operations Research, INFORMS, vol. 38(2), pages 358-388, May.
    2. Han, Chulwoo & Park, Frank C., 2022. "A geometric framework for covariance dynamics," Journal of Banking & Finance, Elsevier, vol. 134(C).
    3. Helton, J.C. & Hansen, C.W. & Sallaberry, C.J., 2014. "Conceptual structure and computational organization of the 2008 performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 122(C), pages 223-248.
    4. Pierre L’Ecuyer & Bruno Tuffin, 2011. "Approximating zero-variance importance sampling in a reliability setting," Annals of Operations Research, Springer, vol. 189(1), pages 277-297, September.
    5. Pierre L'Ecuyer & Christiane Lemieux, 2000. "Variance Reduction via Lattice Rules," Management Science, INFORMS, vol. 46(9), pages 1214-1235, September.
    6. Reiichiro Kawai, 2008. "Adaptive Monte Carlo Variance Reduction for Lévy Processes with Two-Time-Scale Stochastic Approximation," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 199-223, June.
    7. Hejin Wang & Zhan Zheng, 2024. "Randomly Shifted Lattice Rules with Importance Sampling and Applications," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    8. Xueping Wu & Jin Zhang, 1999. "Options on the minimum or the maximum of two average prices," Review of Derivatives Research, Springer, vol. 3(2), pages 183-204, May.
    9. Shih-Kuei Lin & Ren-Her Wang & Cheng-Der Fuh, 2006. "Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(3), pages 261-295, September.
    10. Xiaoqun Wang & Ken Seng Tan, 2013. "Pricing and Hedging with Discontinuous Functions: Quasi-Monte Carlo Methods and Dimension Reduction," Management Science, INFORMS, vol. 59(2), pages 376-389, July.
    11. Lapeyre Bernard & Lelong Jérôme, 2011. "A framework for adaptive Monte Carlo procedures," Monte Carlo Methods and Applications, De Gruyter, vol. 17(1), pages 77-98, January.
    12. Aleksandar Arandjelović & Thorsten Rheinländer & Pavel V. Shevchenko, 2025. "Importance sampling for option pricing with feedforward neural networks," Finance and Stochastics, Springer, vol. 29(1), pages 97-141, January.
    13. Sandeep Juneja & Perwez Shahabuddin, 2001. "Fast Simulation of Markov Chains with Small Transition Probabilities," Management Science, INFORMS, vol. 47(4), pages 547-562, April.
    14. Genin, Adrien & Tankov, Peter, 2020. "Optimal importance sampling for Lévy processes," Stochastic Processes and their Applications, Elsevier, vol. 130(1), pages 20-46.
    15. Bernard Lapeyre & J'er^ome Lelong, 2010. "A framework for adaptive Monte-Carlo procedures," Papers 1001.3551, arXiv.org, revised Jul 2010.
    16. Adam W. Kolkiewicz, 2016. "Efficient Hedging Of Path–Dependent Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-27, August.
    17. dos Reis, Gonçalo & Smith, Greig & Tankov, Peter, 2023. "Importance sampling for McKean-Vlasov SDEs," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    18. Ad Ridder & Bruno Tuffin, 2012. "Probabilistic Bounded Relative Error Property for Learning Rare Event Simulation Techniques," Tinbergen Institute Discussion Papers 12-103/III, Tinbergen Institute.
    19. Ma, Xiaocui & Xi, Fubao, 2017. "Moderate deviations for neutral stochastic differential delay equations with jumps," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 97-107.
    20. Xiaoqun Wang & Ian H. Sloan, 2011. "Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction," Operations Research, INFORMS, vol. 59(1), pages 80-95, February.

    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:inm:ormnsc:v:46:y:2000:i:10:p:1349-1364. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.