基于Windows Azure云并行计算的期权定价SaaS
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摘要
计算速度对于期权交易者至关重要,关系到如何有效地制定价格并评估相应的风险,而云并行计算提供的随收随付制(pay-as-you-go)可以实现低成本运行。在微软云平台Windows Azure的基础上,开发了基于云并行计算的期权定价试点云软件AzureOP,该软件以较低的费用提供了低风险和高速度,并给出了AzureOP对于美式期权价格的模拟结果,绘制了对应的期权价格定价曲线和定价曲面。最后,对云并行计算在金融应用上的优势和不足进行了总结和讨论,同时举例说明了试点云软件AzureOP的具体细节。
The computational speed is important for the crucial for option traders to efficiently decide the price and evaluate the corresponding risk,and cloud parallel computing provides a low-cost implementation by providers' pay-as-you-go policy.Based on Microsoft's cloud platform Windows Azure,developed an option pricing's pilot cloud software-AzureOP,this software provided low risk and high speed with a lower cost,and presented simulation results of AzureOP for American Put,plotted the corresponding option pricing curve and pricing surface. Finally,makes conclusions and discusses the advantage and the disadvantage of computational finance on the cloud,and illustrates the details development of pilot cloud software AzureOP.
The computational speed is important for the crucial for option traders to efficiently decide the price and evaluate the corresponding risk,and cloud parallel computing provides a low-cost implementation by providers' pay-as-you-go policy.Based on Microsoft's cloud platform Windows Azure,developed an option pricing's pilot cloud software-AzureOP,this software provided low risk and high speed with a lower cost,and presented simulation results of AzureOP for American Put,plotted the corresponding option pricing curve and pricing surface. Finally,makes conclusions and discusses the advantage and the disadvantage of computational finance on the cloud,and illustrates the details development of pilot cloud software AzureOP.
引文
[1] Armbrust M,Fox A,Griffith R,et al. A view of cloud computing[J].Communications of the ACM,2010,53(4):50-58.
[2] Buyya R,Yeo C S,Venugopal S,et al. Cloud computing and emerging IT platforms:Vision,hype,and reality for delivering computing as the5th utility[J]. Future Generation Computer Systems,2009,25(6):599-616.
[3] Marston S,Li Z,Bandyopadhyay S,et al. Cloud computing:the business perspective[J]. Decision Support Systems,2011,51(1):176-189.
[4] Dillon T,Wu Chen,Chang E. Cloud computing:issues and challenges[C]//Proc of the 24th IEEE International Conference on Advanced Information Networking and Applications. Piscataway,NJ:IEEE Press,2010:27-33.
[5] Doan D. A developer’s survey on different cloud platforms[D]. San Diego:University of California,2009.
[6] Avetisyan A I,Campbell R,Gupta I,et al. Open cirrus:a global cloud computing testbed[J]. Computer,2010,43(4):35-43.
[7] Gunarathne T,Wu T L,Choi J Y,et al. Cloud computing paradigms for pleasingly parallel biomedical applications[J]. Concurrency&Computation Practice&Experience,2011,23(17):2338-2354.
[8] Gunarathne T,Zhang Bingjing,Wu T L,et al. Portable parallel programming on cloud and HPC:scientific applications of Twister4Azure[C]//Proc of the 4th IEEE International Conference on Utility and Cloud Computing. Washington DC:IEEE Computer Society,2012:97-104.
[9] Lu Wei,Jackson J,Barga R. Azure Blast:a case study of developing science applications on the cloud[C]//Proc of the 19th ACM International Symposium on High Performance Distributed Computing. New York:ACM Press,2010:413-420.
[10] Qiu Xiaohong,Ekanayake J,Beason S,et al. Cloud technologies for bioinformatics applications[C]//Proc of the 2nd Workshop on ManyTask Computing on Grids and Supercomputers. New York:ACM Press,2009:6.
[11]Pratt B,Howbert J J,Tasman N I,et al. MR-Tandem:parallel X!Tandem using Hadoop MapReduce on Amazon Web services[J].Bioinformatics,2012,28(1):136-137.
[12]Waltz E. 1000 genomes on Amazon’s cloud[J]. Nature Biotechnology,2012,30:376-376.
[13]Zhang Lu,Gu Shengchang,Liu Yuan,et al. Gene set analysis in the cloud[J]. Bioinformatics,2012,28(2):294-295.
[14]Jourdren L,Bernard M,Dillies M A,et al. Eoulsan:a cloud computing-based framework facilitating high throughput sequencing analyses[J]. Bioinformatics,2012,28(11):1542-1543.
[15]Langmead B,Salzberg S L. Fast gapped-read alignment with Bowtie 2[J]. Nature Methods,2012,9(4):357-359.
[16]Hong Dongwan,Rhie A,Park S S,et al. FX:an RNA-Seq analysis tool on the cloud[J]. Bioinformatics,2012,28(5):721-723.
[17]Humphrey M,Hill Z,Van Ingen C,et al. Assessing the value of cloudbursting:a case study of satellite image processing on Windows Azure[C]//Proc of the 7th IEEE International Conference on E-Science.Piscataway,NJ:IEEE Press,2011:126-133.
[18] Subramanian V,Wang Liqiang,Lee E J,et al. Rapid processing of synthetic seismograms using Windows Azure cloud[C]//Proc of the2nd IEEE International Conference on Cloud Computing Technology&Science. Piscataway,NJ:IEEE Press,2011:193-200.
[19]Fernández-Carde Osa G,Torre-Díez I D L,López-Coronado M,et al.Analysis of cloud-based solutions on EHRs systems in different scenarios[J]. Journal of Medical Systems,2012,36(6):3777-3782.
[20]Lawton G. Cloud streaming brings video to mobile devices[J]. Computer,2012,45(2):14-16.
[21] Bohner M,Zheng Yao. On analytical solutions of the Black-Scholes equation[J]. Applied Mathematics Letters,2009,22(3):309-313.
[22]Hull J C. Options,futures and other derivatives[M].[S. l.]:Pearson,2009.
[23]Gerbessiotis A V. Parallel option price valuations with the explicit finite difference method[J]. International Journal of Parallel Programming,2010,38(2):159-182.
[24] Bollerslev T,Gibson M,Zhou Hao. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities[J]. Journal of Econometrics,2011,160(1):235-245.
[25]Liu Qiang. Pricing American options by canonical least-squares Monte Carlo[J]. Journal of Futures Markets,2008,30(2):175-187.
[26]Jasra A,Del Moral P. Sequential Monte Carlo methods for option pricing[J]. Stochastic Analysis&Applications,2011,29(2):292-316.
[27]Devreese J P A,Lemmens D,Tempere J. Path integral approach to Asian options in the Black-Scholes model[J]. Physica A:Statistical Mechanics&its Applications,2010,389(4):780-788.
[28]Kohler M,Krzyzak A,Todorovic N. Pricing of high-dimensional American options by neural networks[J]. Mathematical Finance,2010,20(3):383-410.
[29]Swarztrauber P N. A parallel algorithm for solving general tridiagonal equations[J]. Mathematics of Computation,1979,33(145):185-199.
[30]Mattor N,Williams T J,Hewett D W. Algorithm for solving tridiagonal matrix problems in parallel[J]. Parallel Computing,1995,21(11):1769-1782.
[31]Lin H X. A unifying graph model for designing parallel algorithms for tridiagonal systems[J]. Parallel Computing,2001,27(7):925-939.
[32]张帆.两类期权定价模型有限差分的并行计算[D].保定:华北电力大学,2014.(Zhang Fan. Finite difference parallel computing of solving two option pricing models[D]. Baoding:North China Electric Power University,2014.)
[33]郭瑶瑶.两类期权定价模型有限差分并行计算的新方法研究[D].保定:华北电力大学,2016.(Guo Yaoyao. Research on new methods of finite difference parallel computing for solving two option pricing models[D]. Baoding:North China Electric Power University,2016.)
[34]蒋晓蓝.用GPU实现基于LSM算法的美式期权定价[J].科技创新导报,2013,18:34-35.(Jiang Xiaolan. Realization of American option pricing based on LSM algorithm with GPU[J]. Science and Technology Consulting Herald,2013,18:34-35.)
[35]刘颖,江一鸣.带不对称跳的期权定价扩散模型的参数估计[J].南开大学学报,2017,50(6):66-73.(Liu Ying,Jiang Yiming. The Parameter estimation of asymmetric jump diffusion model for optional pricing[J]. Acta Scientiarum Naturalium University Nankaiensis,2017,50(6):66-73.)
[36]陈潇.拟蒙特卡罗方法在期权定价中的应用[D].长春:吉林大学,2017.(Chen Xiao. Ouasi-Monte Carlo method in option pricing[D]. Changchun:Jilin University,2017.)
[37]代斌.基于GPU的倒向随机微分方程的期权定价的并行算法研究[D].济南:山东大学,2012.(Dai Bin. Research of parallel option pricing with BSDE method on GPU[D]. Jinan:Shandong University,2012.)
[38]刘源,张金燕.云计算技术在美式期权定价中的应用[J].电子技术与软件工程,2016(15):205.(Liu Yuan,Zhang Jinyan. Application of cloud computing technology in American option pricing[J]. Electronic Technology and Software Engineering,2016(15):205.)
[39]王德原.基于期权和拍卖机制的云计算定价模型的研究[D].北京:北京邮电大学,2013.(Wang Deyuan. Research on cloud computing pricing model based on option and auction mechanism[D].Beijing:Beijing University of Posts and Telecommunications,2013.)
[2] Buyya R,Yeo C S,Venugopal S,et al. Cloud computing and emerging IT platforms:Vision,hype,and reality for delivering computing as the5th utility[J]. Future Generation Computer Systems,2009,25(6):599-616.
[3] Marston S,Li Z,Bandyopadhyay S,et al. Cloud computing:the business perspective[J]. Decision Support Systems,2011,51(1):176-189.
[4] Dillon T,Wu Chen,Chang E. Cloud computing:issues and challenges[C]//Proc of the 24th IEEE International Conference on Advanced Information Networking and Applications. Piscataway,NJ:IEEE Press,2010:27-33.
[5] Doan D. A developer’s survey on different cloud platforms[D]. San Diego:University of California,2009.
[6] Avetisyan A I,Campbell R,Gupta I,et al. Open cirrus:a global cloud computing testbed[J]. Computer,2010,43(4):35-43.
[7] Gunarathne T,Wu T L,Choi J Y,et al. Cloud computing paradigms for pleasingly parallel biomedical applications[J]. Concurrency&Computation Practice&Experience,2011,23(17):2338-2354.
[8] Gunarathne T,Zhang Bingjing,Wu T L,et al. Portable parallel programming on cloud and HPC:scientific applications of Twister4Azure[C]//Proc of the 4th IEEE International Conference on Utility and Cloud Computing. Washington DC:IEEE Computer Society,2012:97-104.
[9] Lu Wei,Jackson J,Barga R. Azure Blast:a case study of developing science applications on the cloud[C]//Proc of the 19th ACM International Symposium on High Performance Distributed Computing. New York:ACM Press,2010:413-420.
[10] Qiu Xiaohong,Ekanayake J,Beason S,et al. Cloud technologies for bioinformatics applications[C]//Proc of the 2nd Workshop on ManyTask Computing on Grids and Supercomputers. New York:ACM Press,2009:6.
[11]Pratt B,Howbert J J,Tasman N I,et al. MR-Tandem:parallel X!Tandem using Hadoop MapReduce on Amazon Web services[J].Bioinformatics,2012,28(1):136-137.
[12]Waltz E. 1000 genomes on Amazon’s cloud[J]. Nature Biotechnology,2012,30:376-376.
[13]Zhang Lu,Gu Shengchang,Liu Yuan,et al. Gene set analysis in the cloud[J]. Bioinformatics,2012,28(2):294-295.
[14]Jourdren L,Bernard M,Dillies M A,et al. Eoulsan:a cloud computing-based framework facilitating high throughput sequencing analyses[J]. Bioinformatics,2012,28(11):1542-1543.
[15]Langmead B,Salzberg S L. Fast gapped-read alignment with Bowtie 2[J]. Nature Methods,2012,9(4):357-359.
[16]Hong Dongwan,Rhie A,Park S S,et al. FX:an RNA-Seq analysis tool on the cloud[J]. Bioinformatics,2012,28(5):721-723.
[17]Humphrey M,Hill Z,Van Ingen C,et al. Assessing the value of cloudbursting:a case study of satellite image processing on Windows Azure[C]//Proc of the 7th IEEE International Conference on E-Science.Piscataway,NJ:IEEE Press,2011:126-133.
[18] Subramanian V,Wang Liqiang,Lee E J,et al. Rapid processing of synthetic seismograms using Windows Azure cloud[C]//Proc of the2nd IEEE International Conference on Cloud Computing Technology&Science. Piscataway,NJ:IEEE Press,2011:193-200.
[19]Fernández-Carde Osa G,Torre-Díez I D L,López-Coronado M,et al.Analysis of cloud-based solutions on EHRs systems in different scenarios[J]. Journal of Medical Systems,2012,36(6):3777-3782.
[20]Lawton G. Cloud streaming brings video to mobile devices[J]. Computer,2012,45(2):14-16.
[21] Bohner M,Zheng Yao. On analytical solutions of the Black-Scholes equation[J]. Applied Mathematics Letters,2009,22(3):309-313.
[22]Hull J C. Options,futures and other derivatives[M].[S. l.]:Pearson,2009.
[23]Gerbessiotis A V. Parallel option price valuations with the explicit finite difference method[J]. International Journal of Parallel Programming,2010,38(2):159-182.
[24] Bollerslev T,Gibson M,Zhou Hao. Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities[J]. Journal of Econometrics,2011,160(1):235-245.
[25]Liu Qiang. Pricing American options by canonical least-squares Monte Carlo[J]. Journal of Futures Markets,2008,30(2):175-187.
[26]Jasra A,Del Moral P. Sequential Monte Carlo methods for option pricing[J]. Stochastic Analysis&Applications,2011,29(2):292-316.
[27]Devreese J P A,Lemmens D,Tempere J. Path integral approach to Asian options in the Black-Scholes model[J]. Physica A:Statistical Mechanics&its Applications,2010,389(4):780-788.
[28]Kohler M,Krzyzak A,Todorovic N. Pricing of high-dimensional American options by neural networks[J]. Mathematical Finance,2010,20(3):383-410.
[29]Swarztrauber P N. A parallel algorithm for solving general tridiagonal equations[J]. Mathematics of Computation,1979,33(145):185-199.
[30]Mattor N,Williams T J,Hewett D W. Algorithm for solving tridiagonal matrix problems in parallel[J]. Parallel Computing,1995,21(11):1769-1782.
[31]Lin H X. A unifying graph model for designing parallel algorithms for tridiagonal systems[J]. Parallel Computing,2001,27(7):925-939.
[32]张帆.两类期权定价模型有限差分的并行计算[D].保定:华北电力大学,2014.(Zhang Fan. Finite difference parallel computing of solving two option pricing models[D]. Baoding:North China Electric Power University,2014.)
[33]郭瑶瑶.两类期权定价模型有限差分并行计算的新方法研究[D].保定:华北电力大学,2016.(Guo Yaoyao. Research on new methods of finite difference parallel computing for solving two option pricing models[D]. Baoding:North China Electric Power University,2016.)
[34]蒋晓蓝.用GPU实现基于LSM算法的美式期权定价[J].科技创新导报,2013,18:34-35.(Jiang Xiaolan. Realization of American option pricing based on LSM algorithm with GPU[J]. Science and Technology Consulting Herald,2013,18:34-35.)
[35]刘颖,江一鸣.带不对称跳的期权定价扩散模型的参数估计[J].南开大学学报,2017,50(6):66-73.(Liu Ying,Jiang Yiming. The Parameter estimation of asymmetric jump diffusion model for optional pricing[J]. Acta Scientiarum Naturalium University Nankaiensis,2017,50(6):66-73.)
[36]陈潇.拟蒙特卡罗方法在期权定价中的应用[D].长春:吉林大学,2017.(Chen Xiao. Ouasi-Monte Carlo method in option pricing[D]. Changchun:Jilin University,2017.)
[37]代斌.基于GPU的倒向随机微分方程的期权定价的并行算法研究[D].济南:山东大学,2012.(Dai Bin. Research of parallel option pricing with BSDE method on GPU[D]. Jinan:Shandong University,2012.)
[38]刘源,张金燕.云计算技术在美式期权定价中的应用[J].电子技术与软件工程,2016(15):205.(Liu Yuan,Zhang Jinyan. Application of cloud computing technology in American option pricing[J]. Electronic Technology and Software Engineering,2016(15):205.)
[39]王德原.基于期权和拍卖机制的云计算定价模型的研究[D].北京:北京邮电大学,2013.(Wang Deyuan. Research on cloud computing pricing model based on option and auction mechanism[D].Beijing:Beijing University of Posts and Telecommunications,2013.)