论文标题
数字选项的多级路径分支
Multilevel Path Branching for Digital Options
论文作者
论文摘要
我们提出了一个新的基于蒙特卡洛的估计器，该估计量具有由随机微分方程（SDE）建模的资产。新的估计器基于重复的路径分裂，并依赖于共享布朗路径部分的基础SDE的近似路径的相关性。将这个新的估计器与多级蒙特卡洛（MLMC）相结合，导致具有计算复杂性的估计器，该估计值与MLMC估计器的复杂性相似，当时使用Lipschitz收益的选项。 该预印本包括详细的计算和证明（灰色），这些计算和证明未经同行评审，也未包含在已发表的文章中。
We propose a new Monte Carlo-based estimator for digital options with assets modelled by a stochastic differential equation (SDE). The new estimator is based on repeated path splitting and relies on the correlation of approximate paths of the underlying SDE that share parts of a Brownian path. Combining this new estimator with Multilevel Monte Carlo (MLMC) leads to an estimator with a computational complexity that is similar to the complexity of a MLMC estimator when applied to options with Lipschitz payoffs. This preprint includes detailed calculations and proofs (in grey colour) which are not peer-reviewed and not included in the published article.
