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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
dc.contributor.author | Ching-Wen Chen | en |
dc.contributor.author | 陳鏡文 | zh_TW |
dc.date.accessioned | 2021-06-16T08:05:46Z | - |
dc.date.available | 2019-07-08 | |
dc.date.copyright | 2014-07-08 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-06-24 | |
dc.identifier.citation | [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, Princeton [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 Politi Econ 81(3): 637–654 [5] Bochkanov S (1999) ALGLIB. http://www.alglib.net. Accessed 13 Feb 2014 [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] Boyle P (1977) Options: A Monte Carlo approach. J Financ Econ 4(3): 323–338 [8] Boyle P, Broadie M, Glasserman P (1997) Monte Carlo methods for security pricing. J Econ Dyn Control 21(8): 1267–1321 [9] 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 [10] Clement E, Lamberton D, Protter P (2002) An analysis of a least squares regression method for American option pricing. Financ Stoch 6(3): 449–471 [11] Cox JC, Ross S, Rubinstein M (1979) Option pricing: A simplified approach. J Financ Econ 7(3): 229–263 [12] Dongarra J (1993) TOP500 Supercomputer Sites. http://www.top500.org. Accessed 3 April 2014 [13] Eager DL, Zahorjan J, Lazowska ED (1989) Speedup versus efficiency in parallel systems. IEEE Tran Comput 38(3): 408–423 [14] Fox GC, Williams RD, Messina GC (1994) Parallel computing works! Morgan Kaufmann, Burlington [15] Haug EG (2007) The complete guide to option pricing formulas, 2nd edn. McGraw-Hill, New York [16] Hennessy JL, Patterson DA (2011) Computer architecture: A quantitative approach, 5th edn. Morgan Kaufmann, Burlington [17] Letourneau P, Stentoft L (2014) Refining the least squares Monte Carlo method by imposing structure. Quant Financ 14(3): 495–508 [18] Longstaff FA, Schwartz ES (2001) Valuing American options by simulation: A simple least-squares approach. Rev Financ Stud 14(1): 113–147 [19] Moreno M, Navas JF (2003) On the robustness of least-squares Monte Carlo (LSM) for pricing American derivatives. Rev Deriv Res 6(2): 107–128 [20] Stentoft L (2004) Convergence of the least squares Monte Carlo approach to American option valuation. Manag Sci 50(9): 1193–1203 [21] Sunderam VS (1990) PVM: A framework for parallel distributed computing. Concurr: Pract Exp 2(4): 315–339 [22] Wilkinson B, Allen M (1999) Parallel programming: Techniques and applications using networked workstations and parallel computers. Prentice Hall, Upper Saddle River | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58089 | - |
dc.description.abstract | 本研究以平行運算加速最小平方蒙地卡羅法在財金領域的應用。我們根據路徑將最小平方蒙地卡羅法分割成多個子問題,每一個子問題由一個從屬行程獨立運算,直到結果計算完成後再傳回主行程,由主行程平均所有結果來得到最後的評價。此方法將最小平方蒙地卡羅法轉換成尷尬平行的問題。本研究使用平行虛擬機(Parallel Virtual Machine)及ALGLIB實作,並對美式賣權進行評價。由實驗結果可得知,使用平行運算於最小平方蒙地卡羅法時,評價結果並不會失真,同時速度也能有效率的提升,我們使用8台機器共64個行程得到了55倍的加速。本研究所提出的方法可延伸至使用最小平方蒙地卡羅法評價更複雜的衍生性金融商品。 | zh_TW |
dc.description.abstract | This thesis accelerates the popular least-squares Monte Carlo method (LSM) in finance with parallel computing. Several processes are created to solve LSM. Each process solves a smaller version of LSM independently before averaging the values calculated by all the processes. This methodology turns LSM into an embarrassingly parallel problem. The program is implemented using Parallel Virtual Machine (PVM) and ALGLIB. This thesis focuses on the pricing of American put options. Our proposed method gives accurate option prices with excellent speedups and achieves a speedup of 55 using 64 processes with 8 machines. The same methodology is expected to yield excellent speedups for LSM when applied to more complex financial derivatives. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T08:05:46Z (GMT). No. of bitstreams: 1 ntu-103-R01922005-1.pdf: 2162602 bytes, checksum: c5a07977b95efeeb5879a27369583222 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 謝 辭 i
摘 要 ii Abstract iii Chapter 1 Introduction 1 Chapter 2 The Least-Squares Monte Carlo Method 9 Chapter 3 Methodologies and Setups 12 3.1 Path Generation 12 3.2 Least-Squares Regression 12 3.3 Parallelization 13 Chapter 4 Experimental Results 15 4.1 Option Valuation 15 4.2 Speedup and Efficiency 21 Chapter 5 Conclusion 28 References 30 | |
dc.language.iso | en | |
dc.title | 使用平行運算加速最小平方蒙地卡羅法 | zh_TW |
dc.title | Accelerating the Least-Squares Monte Carlo Method
with Parallel Computing | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 戴天時(Tian-Shyr Dai),張經略(Ching-Lueh Chang),王釧茹(Chuan-Ju Wang) | |
dc.subject.keyword | 最小平方蒙地卡羅法,平行運算,選擇權評價,尷尬平行,平行虛擬機, | zh_TW |
dc.subject.keyword | least-squares Monte Carlo,parallel computing,option pricing,embarrassingly parallel,Parallel Virtual Machine, | en |
dc.relation.page | 32 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-06-25 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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