Abstract: An option, a type of a financial derivative, is a contract that creates an opportunity for a market player to avoid risks involved in investing, especially in equities. An investor desires to know the accurate value of an option before entering into a contract to buy/sell the underlying asset (stock). There are various techniques that try to simulate real market conditions in order to price an option. However, most of them achieved limited success due to high uncertainty in price behavior of the underlying asset. In this study, we propose two new variants of Firefly algorithm to compute accurate worth of European and American option contracts and compare them with popular option-pricing models (such as Black-Scholes-Merton, binomial lattice, Monte-Carlo, etc.) and real market data. In this study, we have first modeled option pricing as a multi-objective optimization problem, where we introduce the pay-off and probability of achieving that pay-off as the main optimization objectives. Then, we propose to use a latest nature-inspired algorithm that uses the bioluminescence of Fireflies to simulate the market conditions. We propose an adaptive weighted-sum based Firefly algorithm and non-dominant sorting Firefly algorithm to find Pareto optimal solutions for the option-pricing problem. Using these algorithm(s), we successfully compute complete Pareto front of option prices for a number of option contracts from real market (Bloomberg data). Also, we show that one of the points on the Pareto front represents the option value within 1-2% error of the real data (Bloomberg). Moreover, with our experiments, we show that an investor may utilize the results in the Pareto front for deciding to get into an option contract and can evaluate the worth of a contract tuned to his/her risk ability. This implies that our proposed multi-objective model and firefly algorithm could be used in real markets for pricing options at different levels of accuracy.
Speaker's Bio: Dr. Ruppa K. Thulasiram (Tulsi) is a Professor with the Dept. of Computer Science, Univ. of Manitoba, Canada. He received his PhD from Indian Institute of Science, Bangalore, India and spent years at Concordia University, Canada; Georgia Institute of Technology, USA; and University of Delaware, USA, as Post-doc, Research Staff and Research Faculty before taking up a position at U. Manitoba. Tulsi's current research interests include Computational Finance, Cloud Computing, Blockchain applications in finance and related areas. He has written many papers in the areas of High Temperature Physics, Gas Dynamics, Computational Finance, Grid/Cloud computing, and Blockchain applications. He has graduated many students with MSc and PhD theses and has received many best paper awards in reputed conferences. He holds a patent for true random generators along with his students and colleagues. Tulsi has developed a curriculum for cross-disciplinary computational finance area for both graduate and senior undergraduate levels and has been teaching for the past several years. Tulsi has organized many conferences and has been editor and guest editor with many journals. He is a IEEE senior member and a member with few other professional societies such as ACM, ASAC, etc.