We present new high order approximations schemes for the Cox-Ingersoll-Ross (CIR) process that are obtained by using a recent technique developed by Alfonsi and Bally (2021) for the approximation of semigroups. The idea consists in using a suitable combination of discretization schemes calculated on different random grids to increase the order of convergence. This technique coupled with the second order scheme proposed by Alfonsi (2010) for the CIR leads to weak approximations of order $2k$, for all $k\in\mathbb{N}^*$. Despite the singularity of the square-root volatility coefficient, we show rigorously this order of convergence under some restrictions on the volatility parameters. We illustrate numerically the convergence of these approximations for the CIR process and for the Heston stochastic volatility model and show the computational time gain they give.
翻译:我们利用 Alfonsi 和 Bally (2021) 最近发展的半群逼近技术,提出了 Cox-Ingersoll-Ross (CIR) 过程的新的高阶逼近方案。该想法是利用不同随机网格上的合适组合离散化方案来增加收敛阶数。该技术与 Alfonsi (2010) 提出的二阶方案相结合,能够得到所有 $k\in\mathbb{N}^*$ 的 $2k$ 阶弱逼近。尽管存在方根波动率系数的奇点,我们在一些波动率参数的限制下,证明了这种收敛阶数的严格性。我们数值地演示了这些逼近对于 CIR 过程和 Heston 随机波动率模型的收敛性,并展示了它们节约的计算时间。