Pricing Financial Option as a Multi-Objective Optimization Problem Using Firefly Algorithms
| dc.contributor.author | Singh, Gobind Preet | |
| dc.contributor.examiningcommittee | Irani, Pourang (Computer Science) Gajpal, Yuvraj (Supply Chain Management) | en_US |
| dc.contributor.supervisor | Thulasiram, Ruppa K (Computer Science) | en_US |
| dc.date.accessioned | 2016-09-01T16:19:42Z | |
| dc.date.available | 2016-09-01T16:19:42Z | |
| dc.date.issued | 2016 | |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | An option, a type of a financial derivative, is a contract that creates an opportunity for a market player to avoid risks involved in investing, especially in equities. An investor desires to know the accurate value of an option before entering into a contract to buy/sell the underlying asset (stock). There are various techniques that try to simulate real market conditions in order to price or evaluate an option. However, most of them achieved limited success due to high uncertainty in price behavior of the underlying asset. In this study, I propose two new Firefly variant algorithms to compute accurate worth for European and American option contracts and compare them with popular option pricing models (such as Black-Scholes-Merton, binomial lattice, Monte-Carlo, etc.) and real market data. In my study, I have first modelled the option pricing as a multi-objective optimization problem, where I introduced the pay-off and probability of achieving that pay-off as the main optimization objectives. Then, I proposed to use a latest nature-inspired algorithm that uses the bioluminescence of Fireflies to simulate the market conditions, a first attempt in the literature. For my thesis, I have proposed adaptive weighted-sum based Firefly algorithm and non-dominant sorting Firefly algorithm to find Pareto optimal solutions for the option pricing problem. Using my algorithm(s), I have successfully computed complete Pareto front of option prices for a number of option contracts from the real market (Bloomberg data). Also, I have shown that one of the points on the Pareto front represents the option value within 1-2 % error of the real data (Bloomberg). Moreover, with my experiments, I have shown that any investor may utilize the results in the Pareto fronts for deciding to get into an option contract and can evaluate the worth of a contract tuned to their risk ability. This implies that my proposed multi-objective model and Firefly algorithm could be used in real markets for pricing options at different levels of accuracy. To the best of my knowledge, modelling option pricing problem as a multi-objective optimization problem and using newly developed Firefly algorithm for solving it is unique and novel. | en_US |
| dc.description.note | October 2016 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/31618 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Option Pricing | en_US |
| dc.subject | Multi-Objective Optimization | en_US |
| dc.subject | Firefly Algorithm | en_US |
| dc.subject | American Option | en_US |
| dc.subject | European option | en_US |
| dc.subject | Pareto front | en_US |
| dc.title | Pricing Financial Option as a Multi-Objective Optimization Problem Using Firefly Algorithms | en_US |
| dc.type | master thesis | en_US |