Ant Colony Optimization to price exotic options

Abstract

Option pricing is one of the challenging problems in finance. Finding the best time to exercise an option is a even more challenging problem, especially since the price of the underlying assets change rapidly. In this work, we study complex path dependent options by exploiting and extending a novel idea that we proposed earlier using a nature inspired meta-heuristic algorithm. Ant Colony Optimization (ACO). ACO has been used extensively in combinatorial optimization problems and recently in dynamic applications such as mobile ad-hoc networks where the objective is find a shortest path. However, in finance, especially in option pricing, we look for best time to exercise an option. Specifically, we use ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit from his/her investment. Our algorithm and implementation suggests a better way to price options than traditional techniques such as Monte Carlo simulation or binomial lattice algorithm. Our pricing results compare very well with other techniques and at the same time the computational cost is reduced to a large extent.

A Self-adaptive Hybrid Bio-inspired Optimization Algorithm by Using Sigmoidal Function

“…The integration of the nearest neighbor with those algorithms used for risk assessment and classification. On the ACO, S. Kumar [6] introduced a new approach to finding the pricing options on buying and selling the contract to get maximum benefit. The ant trail in ACO represents a set of pricing options.…”