论文标题
计算风险贡献时避免零概率事件
Avoiding zero probability events when computing Value at Risk contributions
论文作者
论文摘要
本文关注的是，当选择风险度量作为危险价值（VAR）时，通用多元模型的风险分配过程。我们将传统欧拉的贡献从期望的条件下重新铸造，这是零概率与涉及条件期望的比率的事件，其条件性事件具有严格的积极概率。我们得出了各种参数模型的VAR贡献表示形式的分析形式。我们的数值实验表明，使用这种新颖表示的估计器在偏差和方差方面优于标准的蒙特卡洛估计器。此外，与现有的估计器不同，在参数设置下，提出的估计器不含超参数。
This paper is concerned with the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). We recast the traditional Euler contributions from an expectation conditional on an event of zero probability to a ratio involving conditional expectations whose conditioning events have strictly positive probability. We derive an analytical form of the proposed representation of VaR contributions for various parametric models. Our numerical experiments show that the estimator using this novel representation outperforms the standard Monte Carlo estimator in terms of bias and variance. Moreover, unlike the existing estimators, the proposed estimator is free from hyperparameters under a parametric setting.
