A New Method of Exploring the Parameters of Heston Option Pricing Model: Multi-Population Genetic Algorithm

The Heston option pricing model is fundamental for valuing financial derivatives. This paper enhances parameter estimation for the Heston model by extending the traditional Genetic Algorithm GA . While GA is effective for optimization, it often suffers from slow convergence and local minima. To address these issues, we introduce a multi-population Genetic Algorithm MPGA , which improves convergence stability and preserves the optimal solution. Empirical analysis of Shanghai 50ETF and Hang Seng Index options demonstrates that: 1 MPGA is an effective and reliable method for parameter estimation in the Heston model 2 the ask-bid weighting scheme significantly outperforms equal weighting in this context 3 the Heston model calibrated with MPGA achieves higher accuracy compared to traditional approaches and 4 the proposed method performs effectively in both developed and developing markets.