Parameter calibration of stochastic volatility Heston’s model: Constrained optimization vs. differential evolution

This paper calibrates through loss functions the parameters of Heston’s stochastic volatility model by using two different methods: minimizing a nonlinear objective function (a loss function) with constraints on the values of the parameter and using a differential evolution algorithm. Both methods are applied to implied volatilities on the Mexican Stock Exchange Index with four maturities and twenty-eight strike prices. The selection criterion for the parameters is minimizing the value of the mean square error of the implied volatility. The first method has a better performance with less error and time. However, empirical results show that for both methods the adjustment of implied volatilities is better for options with long-term maturities than for short-term maturities.