MSpace at the University of Manitoba >
Faculty of Graduate Studies (Electronic Theses and Dissertations) >
FGS - Electronic Theses & Dissertations (Public) >
Please use this identifier to cite or link to this item:
|Title: ||A Parallel Particle Swarm Optimization Algorithm for Option Pricing|
|Authors: ||Prasain, Hari|
|Supervisor: ||Thulasiraman, Parimala(Computer Science) Thulasiram, Ruppa(Computer Science)|
|Examining Committee: ||Irani, Pourang (Computer Science) Appadoo, Srimantoorao (Supply Chain Management)|
|Graduation Date: ||October 2010|
option pricing algorithm
|Issue Date: ||19-Jul-2010|
|Citation: ||Hari Prasain, Parimala Thulasiraman, Ruppa K. Thulasiram and Girish K. Jha, Particle Swarm Optimization Algorithm for Option Pricing , ACM Genetic and Evolutionary Computation Conference (GECCO), Portland, OR, July 2010|
Hari Prasain, Parimala Thulasiraman, Ruppa K. Thulasiram and Girish K. Jha, Particle Swarm Optimization Algorithm for Option Pricing , IEEE 3rd International Workshop on Parallel and Distributed Computing in Finance, Proc. of the 24th IEEE/ACM Intl. Parallel and Distributed Processing Symposium, April 19-23, 2010, Atlanta, GA, USA.
Hari Prasain, Parimala Thulasiraman, Ruppa K. Thulasiram and Girish K. Jha. Performance Evaluation of PSO-based Algorithm for Option Pricing on Homogeneous Multi-Core Architecture, IASTED International Conference on Computational Intelligence (CI 2010), August 25, 2010, Lahaina, USA.
|Abstract: ||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
conditions, there are no closed form solutions available except
for simple styles of options such as, European options.
Due to the complex nature of the governing mathematics, several
numerical approaches have been proposed in the past to price American style and
other complex options approximately.
Bio-inspired and nature-inspired algorithms have been considered for
solving large, dynamic and complex scientific and engineering problems.
These algorithms are inspired by techniques developed by the insect
societies for their own survival. Nature-inspired algorithms, in particular,
have gained prominence in real world optimization problems such as in mobile ad hoc
networks. The option pricing problem fits very well into this category
of problems due to the ad hoc nature of the market. Particle swarm
optimization (PSO) is one of the novel global search algorithms based
on a class of nature-inspired techniques known as swarm intelligence.
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
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.|
|Appears in Collections:||FGS - Electronic Theses & Dissertations (Public)|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.