交叉熵蝙蝠算法求解期权定价模型参数估计问题

Calibrating option pricing models with cross entropy bat algorithm

期权定价模型的参数估计问题通常是非线性优化问题,且是非凸优化问题,经典的优化方法已不再适用。为此探寻用交叉熵蝙蝠算法来求解Merton跳-扩散模型、Heston随机波动模型和Bates带跳的随机波动模型的参数估计问题。实证结果表明该方法是有效可行的。

Parameter estimation of option pricing model is usually a nonlinear optimization problem with no convex,which leads to the classical optimization method cannot be applied. Based on cross entropy bat algorithm,we studied howto solve parameter estimation problems of option pricing models such as Merton’s jump-diffusion model,Heston’s stochastic volatility model and Bates’s stochastic volatility with jump model. The empirical results showthat the cross entropy bat algorithm is feasible and effective for solving the parameter estimation problems of option pricing model.