The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However, until now, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples or need to rely on Monte Carlo simulations. To remedy these problems, we propose a new method for testing VaR models, based on the distribution of the number of runs. It outperforms the existing methods in two main aspects: First, it is exact in finite samples and thus allows for perfect control over the Type 1 error; second, its distribution is available in a closed form, so it does not require simulations before implementation. We show that it is the most adequate current procedure for testing low‐level VaR series, which corresponds to today's regulatory standards.

中文翻译：

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测试风险价值预测的新的基于运行的方法

改革后的巴塞尔框架将风险价值（VaR）作为验证风险模型的基本工具。在此框架内，VaR 独立性测试被认为对于确保金融动荡期间的稳定至关重要。然而，到目前为止，研究人员尚未就选择适当的测试达成一致。可用的程序要么在有限样本中不准确，要么需要依赖蒙特卡罗模拟。为了解决这些问题，我们提出了一种基于运行次数分布的 VaR 模型测试新方法。它在两个主要方面优于现有方法：首先，它在有限样本中是精确的，从而可以完美控制类型1误差；其次，它的分布是以封闭的形式提供的，因此在实现之前不需要进行模拟。我们表明，这是测试低水平 VaR 系列的最合适的当前程序，符合当今的监管标准。