Option pricing using Particle Swarm Optimization

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

et al. 2010

Self Cite

Financial derivatives play significant role in an investor's success. Financial option is one form of derivatives. Option pricing is one of the challenging and fundamental problems of computational finance. Due to highly volatile and dynamic market con- ii Abstract iii In this research, we have designed a sequential PSO based option pricing algorithm using basic principles of PSO. The algorithm is applicable for both European and American options, and handles both constant and variable volatility. We show that our results for European options compare well with Black-Scholes-Merton formula.Since it is very important and critical to lock-in profit making opportunities in the real market, we have also designed and developed parallel algorithm to expedite the computing process.We evaluate the performance of our algorithm on a cluster of multicore machines that supports three different architectures: shared memory, distributed memory, and a hybrid architectures. We conclude that for a shared memory architecture or a hybrid architecture, one-to-one mapping of particles to processors is recommended for performance speedup. We get a speedup of 20 on a cluster of four nodes with 8 dual-core processors per node.

Section: Particle Swarm Optimization (Pso) For Option Pricingmentioning

confidence: 99%

Section: A Sequential Option Pricing Algorithm Using Pso (Spso)mentioning

confidence: 99%

Section: A Sequential Option Pricing Algorithm Using Pso (Spso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A parallel Particle swarm optimization algorithm for option pricing

Prasain

Jha

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

et al. 2010

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

Thulasiram

Concurrency and Computation

Prasain

et al. 2014

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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.

Portfolio Management Using Particle Swarm Optimization on GPU

Sharma

Thulasiram

2012 IEEE 10th International Symposium on Parallel and Distributed Processing With Applications

2012

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Mathematical models like the Black-Scholes-Merton model used to price options approximately for simple and plain options in the form of closed form solution. The market is flooded with various styles of options, which are difficult to price. Numerical techniques used for pricing take exorbitant time for reasonable accuracy in pricing results. Heuristic approaches such as Particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost (time). In this work, we first investigate the characteristics of PSO for option pricing and propose improvements to PSO modeling, which reduces the number of PSO parameters without loss of generality of the financial application under study. We have used our improved PSO (called NPSO) model to price complex chooser option, one of the complicated options in the market. Cooperation among particles of the NPSO helps reach the solution in less time. Interest in diversifying investments stems from the necessity to avert risk involved in any single type of investments. The complex chooser option is shown to exhibit the characteristics of a financial portfolio. As a further study, we have used NPSO for portfolio optimization. We have implemented our NPSO model in the state-of-the-art multi-core Graphics processing units (GPU) platform and show that the computational time can be significantly reduced.