A soft computing approach for option pricing

Vijayalaxmi, . and Adiga, Chandrashekara S and Joshi, H G and Harish, S V (2016) A soft computing approach for option pricing. In: Manipal University Colloquium, 03/03/2016, Manipal University Manipal.

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Abstract

Option pricing is one of the challenging and fundamental problems of computational finance. The paper aims to determine the option price and time to exercise the option. In order to optimize the model, Ant Colony Optimization Technique (ACO), which is one of the soft computing techniques, has been used and complete simulation is done using MATLAB 2015a simulation environment. Emphasis is also given on comparing the proposed method with the other existing models and come up with an optimum model for option pricing which will benefit traders and risk managers to obtain the computed results very fast with high accuracies. The algorithm which uses ACO technique has three important steps. Initially it starts by injecting ants from the valuation date (root). It can take any path based on random behavior. Later, individual ants compute the payoff at each node based on a set expression. As soon as an ant finds a value between the predefined values in the expression, it updates the pheromone density leading to the node. Finally updating the pheromone on the path which has a good node helps in making the path more attractive for other ants to explore in the neighboring areas

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Financial derivatives, Option pricing, Ant Colony Optimization
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Electrical and Electronics
Depositing User: MIT Library
Date Deposited: 12 Jan 2017 09:22
Last Modified: 12 Jan 2017 09:22
URI: http://eprints.manipal.edu/id/eprint/148020

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