A bioinspired algorithm to price options
Sameer Kumar, Ruppa K. Thulasiram, Parimala Thulasiraman · 2008
Computing option prices is a challenging problem. Finding the best time to exercise an option is a even more challenging problem. One has to be watchful for the price changes in the market place and act at the right time. That is, prices need to be policed. This paper proposes a novel idea for pricing options 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. Specifically, we adapt the general ACO algorithm to apply to a totally different application, computational finance, in the current study. We police the prices using ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit out of his/her investment. Our algorithm and implementation suggests a better way to price options than traditional numerical techniques such as binomial lattice algorithm. From our results we conclude that reactive ants may be best suited for long-dated options whose performance can still be improved.