Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Computational Methods in Finance: Option Pricing
IEEE Computational Science & Engineering
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Multithreaded Algorithms for Pricing a Class of Complex Options
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Option Valuation With Generalized Ant Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Performance Evaluation of Parallel Algorithms for Pricing Multidimensional Financial Derivatives
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
Ant Colony Optimization
Parallel Algorithm for Pricing American Asian Options with Multi-Dimensional Assets
HPCS '05 Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications
Adaptive genetic programming for option pricing
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A bioinspired algorithm to price options
Proceedings of the 2008 C3S2E conference
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pricing transmission rights using ant colony optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
Engineering Applications of Artificial Intelligence
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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.