论文标题
带有随机延迟的双变量正常逆高斯过程：有效的模拟和对能源市场的应用
A bivariate Normal Inverse Gaussian process with stochastic delay: efficient simulations and applications to energy markets
论文作者
论文摘要
使用Gardini等人引入的自我分解的下属的概念。 [11]，我们构建了一个新的双变量正常逆高斯过程，可以捕获随机延迟。此外，我们还开发了一种新颖的路径仿真方案，该方案依赖于可自我分解的逆高斯定律与莱维驱动的Ornstein-uhlenbeck过程之间的数学联系，并具有逆高斯平稳分布。我们表明，我们的方法可以改进张和张[23]中详述的现有仿真方案，因为它不依赖于接受拒绝方法。最终，这些结果应用于能源市场的建模，并使用拟议的蒙特卡洛方案和傅立叶技术来定价。
Using the concept of self-decomposable subordinators introduced in Gardini et al. [11], we build a new bivariate Normal Inverse Gaussian process that can capture stochastic delays. In addition, we also develop a novel path simulation scheme that relies on the mathematical connection between self-decomposable Inverse Gaussian laws and Lévy-driven Ornstein-Uhlenbeck processes with Inverse Gaussian stationary distribution. We show that our approach provides an improvement to the existing simulation scheme detailed in Zhang and Zhang [23] because it does not rely on an acceptance-rejection method. Eventually, these results are applied to the modelling of energy markets and to the pricing of spread options using the proposed Monte Carlo scheme and Fourier techniques
