A Novel Architecture for Financial Investment Services on a Private Cloud

Saha, Ranjan; Sharma, Bhanu; Thulasiram, Ruppa K.; Thulasiraman, Parimala

doi:10.1007/978-3-319-03859-9_32

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Recently, Saha et al , proposed a framework to provide option pricing Software as a Service on Cloud for investors who are not familiar with any option pricing algorithms. This kind of service could be availed by investors who would like to invest in complex options because with online option pricing tools and calculators, investors could only price simple options.…”

Section: Discussionmentioning

confidence: 99%

“…This kind of service could be availed by investors who would like to invest in complex options because with online option pricing tools and calculators, investors could only price simple options. We hope to incorporate PSOwVV with the already existing bank of numerical algorithms that are rendered as software for investors . Also, we plan to incorporate the parallel PSOwVV into this framework for real time pricing solutions.…”

Section: Discussionmentioning

confidence: 99%

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Nature‐inspired soft computing for financial option pricing using high‐performance analytics

Thulasiram

Thulasiraman

Prasain

et al. 2014

Concurrency and Computation

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SUMMARYHigh-performance computing has witnessed the push towards computer hardware design in the past decade. Many real world problems are both data and compute intensive. Designing efficient algorithms is important to make effective use of the hardware resources for fast data analysis. Finance is one application that will benefit from these supercomputers. Options are instruments that give opportunity to profit from market movements without making large investments. However, understanding the asset price behavior and making a decision to enter into an option contract is quite challenging, called option pricing problem, because underlying asset price might vary violently. In this paper, we propose a nature-inspired soft computing, meta-heuristic, particle swarm optimization (PSO) algorithm to price options. We modify the PSO algorithm and incorporate varying volatility parameters to price options. The proposed algorithm, PSO with Varying Volatility (PSOwVV), is experimented with various PSO and financial parametric conditions. We also develop a parallel PSOwVV algorithm and implement on a distributed shared memory multi-core machine. We show that the parallel algorithm performs well when the number of particles is linearly proportional to the number of processors. The parallel algorithm achieves a speedup of approximately 20 with 64 particles on a four node hybrid cluster.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%