Efficient option pricing in the rough Heston model using weak simulation schemes
提出一种针对粗糙赫斯顿模型的高效弱模拟方案，适用于标准与超粗糙情形，数值实验显示二阶弱收敛且计算成本随步长线性增长，优于现有方案。
We provide an efficient and accurate simulation scheme for the rough Heston model in the standard (H>0) as well as the hyper-rough regime (H>−1/2). The scheme is based on low-dimensional Markovian approximations of the rough Heston process derived in [Bayer and Breneis, arXiv:2309.07023], and provides a weak approximation to the rough Heston process. Numerical experiments show that the new scheme exhibits second-order weak convergence, while the computational cost increases linearly with respect to the number of time steps. In comparison, existing schemes based on discretization of the underlying stochastic Volterra integrals such as Gatheral's HQE scheme show a quadratic dependence of the computational cost. Extensive numerical tests for standard and path-dependent European options and Bermudan options show the method's accuracy and efficiency.