ACM Home Page
Please provide us with feedback. Feedback
State of the art tutorial II: simulations for financial engineering: efficient simulations for option pricing
Full text PdfPdf (184 KB)
Source Winter Simulation Conference archive
Proceedings of the 35th conference on Winter simulation: driving innovation table of contents
New Orleans, Louisiana
SESSION: Risk analysis table of contents
Pages: 258 - 266  
Year of Publication: 2003
ISBN:0-7803-8132-7
Author
Jeremy Staum  Northwestern University, Evanston, IL
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
NIST : National Institute of Standards and Technology
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
ACM: Association for Computing Machinery
(SCS) : The Society for Modeling and 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
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 55,   Citation Count: 0
Additional Information:

abstract   references   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

This paper presents an overview of techniques for improving the efficiency of option pricing simulations, including quasi-Monte Carlo methods, variance reduction, and methods for dealing with discretization error.


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
Avellaneda, M. 1998. Minimum-Relative-Entropy Calibration of Asset-Pricing Models. International Journal of Theoretical and Applied Finance 1: 447--472.
 
2
Boyle, P. 1977. Options: A Monte Carlo Approach. Journal of Financial Economics 4: 323--338.
 
3
Boyle, P., M. Broadie, and P. Glasserman. 1997. Monte Carlo Methods for Security Pricing. Journal of Economic Dynamics and Control 21: 1267--1321.
 
4
Caflisch, R. E., W. Morokoff, and A. Owen. 1997. Valuation of Mortgage Backed Securities Using Brownian Bridges to Reduce Effective Dimension. Journal of Computational Finance 1: 27--46.
 
5
Duffie, D., and P. Glynn. 1995. Efficient Monte Carlo Simulation of Security Prices. Annals of Applied Probability 5: 897--905.
 
6
Glasserman, P. 2003. Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag.
 
7
Glasserman, P., P. Heidelberger, and P. Shahabuddin. 2002. Portfolio Value-at-Risk with Heavy-Tailed Risk Factors. Mathematical Finance 12: 239--270.
 
8
Glasserman, P., and J. Staum. 2001. Stopping Simulated Paths Early. In Proceedings of the 2001 Winter Simulation Conference, ed. B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, 318--324. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers. Available online via <http://www.informs-cs.org/wsc01papers/040.PDF>.
 
9
Glasserman, P., and B. Yu. 2003. Large Sample Properties of Weighted Monte Carlo Estimators. Working paper, Columbia Business School. Available online via <http://www.paulglasserman.com>.
 
10
Herzog, T. N., and G. Lord. 2002. Applications of Monte Carlo Methods to Finance and Insurance. Winstead, Conn.: ACTEX Publications.
 
11
 
12
Karatzas, I., and S. E. Shreve. 1991. Brownian Motion and Stochastic Calculus. 2nd ed. New York: Springer-Verlag.
 
13
Kloeden, P. E., and E. Platen. 1992. Numerical Solution of Stochastic Differential Equations. New York: Springer-Verlag.
 
14
L'Ecuyer, P., and C. Lemieux. 2002. Recent Advances in Randomized Quasi-Monte Carlo Methods. In Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, ed. M. Dror, P. L'Ecuyer, and F. Szidarovszki, 419--474. New York: Kluwer Academic Publishers. Available online via <http://www.iro.umontreal.ca/~lecuyer/papers.html>.
 
15
 
16
 
17
 
18
Owen, A. B. 2002. Necessity of Low Effective Dimension. Working paper, Stanford University. Available online via <http://www-stat.stanford.edu/~owen/reports/>.
 
19
 
20