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Scenario Simulation: Theory and methodology (*)

Author

Listed:
  • Farshid Jamshidian

    (New Products and Strategic Trading, Sakura Global Capital, 42 New Broad Street, London EC2M 1JX, United Kingdom)

  • Yu Zhu

    (Risk Assessment and Control, Sakura Global Capital, 65 East 55 Street, New York, NY 10022, USA)

Abstract

This paper presents a new simulation methodology for quantitative risk analysis of large multi-currency portfolios. The model discretizes the multivariate distribution of market variables into a limited number of scenarios. This results in a high degree of computational efficiency when there are many sources of risk and numerical accuracy dictates a large Monte Carlo sample. Both market and credit risk are incorporated. The model has broad applications in financial risk management, including value at risk. Numerical examples are provided to illustrate some of its practical applications.

Suggested Citation

  • Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
    • Handle: RePEc:spr:finsto:v:1:y:1996:i:1:p:43-67
    Note: received: February 1996; final revision received: June 1996
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    Citations

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    Cited by:

    1. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    2. Chiara Sabelli & Michele Pioppi & Luca Sitzia & Giacomo Bormetti, 2014. "Multi-curve HJM modelling for risk management," Papers 1411.3977, arXiv.org, revised Oct 2015.
    3. Christensen, Jens H.E. & Lopez, Jose A. & Rudebusch, Glenn D., 2015. "A probability-based stress test of Federal Reserve assets and income," Journal of Monetary Economics, Elsevier, vol. 73(C), pages 26-43.
    4. Fabio Trojani & Francesco Audrino, 2005. "Accurate Yield Curve Scenarios Generation using Functional Gradient Descent," Computing in Economics and Finance 2005 14, Society for Computational Economics.
    5. Agata Gemzik-Salwach, 2012. "The Use Of A Value At Risk Measure For The Analysis Of Bank Interest Margins," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 8(4), pages 15-29, February.
    6. Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
    7. Christophe PÉRIGNON & Christophe VILLA, 2002. "Permanent and Transitory Factors Affecting the Dynamics of the Term Structure of Interest Rates," FAME Research Paper Series rp53, International Center for Financial Asset Management and Engineering.
    8. Christodoulakis, George A. & Satchell, Stephen E., 1999. "The simulation of option prices with application to LIFFE options on futures," European Journal of Operational Research, Elsevier, vol. 114(2), pages 249-262, April.
    9. Peter Grundke & Kamil Pliszka, 2018. "A macroeconomic reverse stress test," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 1093-1130, May.
    10. Michael S. Gibson & Matthew Pritsker, 2000. "Improving grid-based methods for estimating value at risk of fixed-income portfolios," Finance and Economics Discussion Series 2000-25, Board of Governors of the Federal Reserve System (U.S.).
    11. Arthur Charpentier & Christophe Villa, 2010. "Generating Yield Curve Stress-Scenarios," Working Papers hal-00550582, HAL.
    12. James Sharpe & Nick Fieller, 2016. "Uncertainty in functional principal component analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2295-2309, September.
    13. Nabil Kahale, 2023. "Simulating Gaussian vectors via randomized dimension reduction and PCA," Papers 2304.07377, arXiv.org.
    14. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.
    15. Damiano Brigo & Cyril Durand, 2014. "An initial approach to Risk Management of Funding Costs," Papers 1410.2034, arXiv.org.
    16. Andrea Beltratti & Andrea Consiglio & Stavros Zenios, 1999. "Scenario modeling for the management ofinternational bond portfolios," Annals of Operations Research, Springer, vol. 85(0), pages 227-247, January.
    17. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    18. Laruent Barras, 2005. "International Conditional Asset Allocation under Real Time Uncertrainty," FAME Research Paper Series rp153, International Center for Financial Asset Management and Engineering.
    19. Xing Jin & Allen X. Zhang, 2006. "Reclaiming Quasi-Monte Carlo Efficiency in Portfolio Value-at-Risk Simulation Through Fourier Transform," Management Science, INFORMS, vol. 52(6), pages 925-938, June.
    20. Roberta Fiori & Simonetta Iannotti, 2006. "Scenario Based Principal Component Value-at-Risk: an Application to Italian Banks' Interest Rate Risk Exposure," Temi di discussione (Economic working papers) 602, Bank of Italy, Economic Research and International Relations Area.
    21. Alessandro Barbiero & Asmerilda Hitaj, 2023. "Discrete approximations of continuous probability distributions obtained by minimizing Cramér-von Mises-type distances," Statistical Papers, Springer, vol. 64(5), pages 1669-1697, October.
    22. Ted Theodosopoulos & Alex Trifunovic, 2006. "Hybrid dynamics for currency modeling," Papers math/0605457, arXiv.org.
    23. Karoline Terán Matamoros & Oscar Molina Tejerina, 2005. "Simulación eficiente del valor de riesgo de un portafolio de acciones del IPSA: Un análisis de componentes principales," Investigación & Desarrollo 0205, Universidad Privada Boliviana, revised Mar 2005.

    More about this item

    Keywords

    Risk analysis · Monte Carlo studies · approximations to distributions;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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