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The Journal of Supercomputing
Published in: The Journal of Supercomputing 9/2015

01-09-2015

Accelerating the least-square Monte Carlo method with parallel computing

Authors: Ching-Wen Chen, Kuan-Lin Huang, Yuh-Dauh Lyuu

Published in: The Journal of Supercomputing | Issue 9/2015

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Abstract

This paper accelerates the critically important least-squares Monte Carlo method (LSM) in financial derivatives pricing with parallel computing. We parallelize LSM with space decomposition, turning it into an embarrassingly parallel algorithm. The program is implemented with Parallel Virtual Machine and ALGLIB. Our method gives accurate option prices with excellent speedup. Although this paper focuses on the pricing of options, the methodology is applicable to much more complex financial derivatives.
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Literature
1.
Amdahl GM (1967) Validity of the single processor approach to achieving large-scale computing capabilities. In: Proceedings of the AFIPS ’67, spring joint computer conference. ACM, New York, pp 483–485
2.
Bellman R (1957) Dynamic programming. Princeton University Press, PrincetonMATH
3.
Benzi J, Damodaran M (2007) Parallel three dimensional direct simulation Monte Carlo for simulating micro flows. In: Proceedings of parallel computational fluid dynamics 2007: implementations and experiences on large scale and grid computing. Springer, Antalya, pp 91–98
4.
Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637–654CrossRefMATH
5.
6.
Bouchard B, Warin X (2010) Monte-Carlo valuation of American options: facts and new algorithms to improve existing methods. In: Carmona RA, Moral PD, Oudjane N (eds) Numerical methods in finance. Springer, Bordeaux
7.
8.
9.
Chaudhary SK (2005) American options and the LSM algorithm: quasirandom sequences and Brownian bridges. J Comput Financ 8:101–115
10.
Choudhury AR, King A, Kumar S, Sabharwal Y (2008) Optimizations in financial engineering: The least-squares Monte Carlo method of Longstaff and Schwartz. In: Proceedings of the 2008 IEEE international parallel and distributed processing symposium. IEEE Computer Society, Miami, pp 1–11
11.
Clément E, Lamberton D, Protter P (2002) An analysis of a least squares regression method for American option pricing. Financ Stoch 6(3):449–471CrossRefMATH
12.
Cox JC, Ross S, Rubinstein M (1979) Option pricing: a simplified approach. J Financ Econ 7(3):229–263CrossRefMATH
13.
Dariusz G, Przemyslaw B, Robert M (2007) Using the LSM approach for derivatives valuation. The LIBOR market model in practice. Wiley, Oxford, pp 229–257
14.
Doan VD, Gaikwad A, Bossy M, Baude F, Stokes-Rees I (2010) Parallel pricing algorithms for multi-dimensional Bermudan/American options using Monte Carlo methods. Math Comput Simula 81(3):568–577MathSciNetCrossRefMATH
15.
16.
Eager DL, Zahorjan J, Lazowska ED (1989) Speedup versus efficiency in parallel systems. IEEE Trans Comput 38(3):408–423CrossRef
17.
Fox GC, Williams RD, Messina GC (1994) Parallel computing works!. Morgan Kaufmann, Burlington
18.
Haug EG (2007) The complete guide to option pricing formulas, 2nd edn. McGraw-Hill, New York
19.
Hennessy JL, Patterson DA (2011) Computer architecture: a quantitative approach, 5th edn. Morgan Kaufmann, Burlington
20.
James L, Dennis G (2009) Multicore computing and scientific discovery. In: Tony H, Stewart T, Kristin T (eds) The fourth paradigm: data-intensive scientific discovery, 1st edn. Microsoft Research, Washington
21.
Létourneau P, Stentoft L (2014) Refining the least squares Monte Carlo method by imposing structure. Quant Financ 14(3):495–508CrossRefMATH
22.
Longstaff FA, Schwartz ES (2001) Valuing American options by simulation: a simple least-squares approach. Rev Financ Stud 14(1):113–147CrossRef
23.
Lyuu YD, Wen KW, Wu YC (2014) Performance of GPU for pricing financial derivatives. J Inf Sci Eng 30(1):141–155
24.
Moreno M, Navas JF (2003) On the robustness of least-squares Monte Carlo (LSM) for pricing American derivatives. Rev Deriv Res 6(2):107–128CrossRefMATH
25.
Richard DL, Stephen JT, John MD (2001) Terascale spectral element dynamical core for atmospheric general circulation models. In: Proceedings of the 2001 ACM/IEEE conference on supercomputing. ACM, New York
26.
Stentoft L (2004) Convergence of the least squares Monte Carlo approach to American option valuation. Manag Sci 50(9):1193–1203CrossRef
27.
Sunderam VS (1990) PVM: a framework for parallel distributed computing. Concurr Pract Exp 2(4):315–339CrossRef
28.
Wan JWL, Lai K, Kolkiewicz AW, Tan KS (2006) A parallel quasi-Monte Carlo approach to pricing American options on multiple assets. Int J High Perform Comput Network 4(5/6):321–330CrossRef
29.
Wilkinson B, Allen M (1999) Parallel programming: techniques and applications using networked workstations and parallel computers. Prentice Hall, Upper Saddle River
30.
Zhang N, Roux A, Zastawniak T (2011) Parallel binomial valuation of American options with proportional transaction costs. In: Temam O, Yew PC, Zang B (eds) Lecture notes in computer science. Advanced parallel processing technologies. Springer, Heidelberg, pp 88–97CrossRef
Metadata
Title
Accelerating the least-square Monte Carlo method with parallel computing
Authors
Ching-Wen Chen
Kuan-Lin Huang
Yuh-Dauh Lyuu
Publication date
01-09-2015
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 9/2015
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-015-1451-7

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