Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm

Quantum effects are a natural phenomenon and just like evolution, or immune processes, can serve as an inspiration for the design of computing algorithms. This study illustrates how a real-valued quantum-inspired evolutionary algorithm (QEA) can be constructed and examines the utility of the resulting algorithm on an important real-world problem, namely the calibration of an Option Pricing model. The results from the algorithm are shown to be robust and sensitivity analysis is carried out on the algorithm parameters, suggesting that there is useful potential to apply QEA to this domain. Categories and Subject Descriptors: Real-World Applications - Finance General Terms: Experimentation

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Kai Fan, Anthony Brabazon, Conall O'Sullivan, Michael O'Neill
• Keywords: Algorithm, GECCO 2007, Important Real-world Problem, Quantum-inspired Evolutionary Algorithm