Option pricing

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Option pricing: simulation in financial engineering

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Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents

Arlington, Virginia

TUTORIAL SESSION: Advanced tutorials table of contents

Pages: 123 - 133

Year of Publication: 2001

ISBN:0-7803-7309-X

Author

Jeremy Staum

Cornell University, Ithaca, NY

Sponsors

INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association

Publisher

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 41, Citation Count: 1

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ABSTRACT

This paper presents an overview of the use of simulation algorithms in the field of financial engineering, assuming on the part of the reader no familiarity with finance and a modest familiarity with simulation methodology, but not its specialist research literature. The focus is on the challenges specific to financial simulations and the approaches that researchers have developed to handle them, although the paper does not constitute a comprehensive survey of the research literature. It offers to simulation researchers, professionals, and students an introduction to an application of increasing significance both within the simulation research community and among financial engineering practitioners.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Andersen, L., and M. Broadie. 2001. A primal-dual simulation algorithm for pricing multi-dimensional American options. Working paper, Graduate School of Business, Columbia University, New York.
	2	Athanassios N. Avramidis , Paul Hyden, Efficiency improvements for pricing American options with a stochastic mesh, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.344-350, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324240]
	3	Björk, T. 1998. Arbitrage Theory in Continuous Time. New York: Oxford University Press.
	4	Boyle, P. 1977. Options: A Monte Carlo approach. Journal of Financial Economics 4:323-338.
	5	Boyle, P., M. Broadie, and P. Glasserman. 1997. Monte Carlo methods for security pricing. Journal of Economic Dynamics and Control 21: 1267-1321.
	6	Mark Broadie , Paul Glasserman, Estimating security price derivatives using simulation, Management Science, v.42 n.2, p.269-285, Feb. 1996 [doi>10.1287/mnsc.42.2.269]
	7	Broadie, M., and P. Glasserman. 1997a. Pricing American-style securities using simulation. Journal of Economic Dynamics and Control 21: 1323-1352.
	8	Broadie, M., and P. Glasserman. 1997b. A stochastic mesh method for pricing high-dimensional American options. Working paper, Graduate School of Business, Columbia University, New York.
	9	Carrière, J. F. 1996. Valuation of the early-exercise price for options using simulations and nonparametric regression. Insurance Mathematics and Economics 19: 19-30.
	10	Clèment, E., D. Lamberton, and P. Protter. 2001. An analysis of the Longstaff-Schwartz algorithm for American option pricing. Working paper, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York.
	11	Duffie, D. 1996. Dynamic Asset Pricing Theory. 2nd ed. Princeton, New Jersey: Princeton University Press.
	12	Duffie, D., and P. Glynn. 1995. Efficient Monte Carlo simulation of security prices. Annals of Applied Probability 5: 897-905.
	13	Glasserman, P., P. Heidelberger, and P. Shahabuddin. 2000. Portfolio value-at-risk with heavy-tailed risk factors. Available online via <http://www.paulglasserman.com>.
	14	Haugh, M., and L. Kogan. 2001. Pricing American options: a duality approach. Working paper, The Wharton School, University of Pennsylvania, Philadelphia.
	15	Hull, J. C. 1999. Options, Futures, and Other Derivatives. 4th ed. Upper Saddle River, New Jersey: Prentice-Hall.
	16	Karatzas, I., and S. E. Shreve. 1991. Brownian Motion and Stochastic Calculus. 2nd ed. New York: Springer-Verlag.
	17	Kloeden, P. E., and E. Platen. 1992. Numerical Solution of Stochastic Differential Equations. New York: Springer-Verlag.
	18	Christiane Lemieux , Pierre L'Ecuyer, On the Use of Quasi-Monte Carlo Methods in Computational Finance, Proceedings of the International Conference on Computational Sciences-Part I, p.607-618, May 28-30, 2001
	19	Longstaff, F. A., and E. S. Schwartz. 2001. Valuing American options by simulation: a simple least-squares approach. Review of Financial Studies 14:113-147.
	20	Harald Niederreiter, Random number generation and quasi-Monte Carlo methods, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1992
	21	Rogers, L. C. G. 2001. Monte Carlo valuation of American options. Available online via <http://www.bath. ac.uk/~maslcgr/papers.html>.
	22	Tsitsiklis, J. N., and B. Van Roy. 1999. Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives. IEEE Transactions on Automatic Control 44: 1840-1851.
	23	Tsitsiklis, J. N., and B. Van Roy. 2000. Regression methods for pricing complex American-style options. Forthcoming, IEEE Transactions on Neural Networks.