An Aggregated Ant Colony Optimization approach for pricing options

Estimating the current cost of an option by predicting the underlying asset prices is the most common methodology for pricing options. Pricing options has been a challenging problem for a long time due to unpredictability in market which gives rise to unpredictability in the option prices. Also the time when the options have to be exercised has to be determined to maximize the profits. This paper proposes an algorithm for predicting the time and price when the option can be exercised to gain expected profits. The proposed method is based on Nature inspired algorithm i.e. Ant Colony Optimization (ACO) which is used extensively in combinatorial optimization problems and dynamic applications such as mobile ad-hoc networks where the objective is to find the shortest path. In option pricing, the primary objective is to find the best node in terms of price and time that would bring expected profit to the investor. Ants traverse the solution space (asset price movements) in the market to identify a profitable node. We have designed and implemented an Aggregated ACO algorithm to price options which is distributed and robust. The initial results are encouraging and we are continuing this work further.