Backtesting value-at-risk : a GMM duration-based test
Backtesting value-at-risk : a GMM duration-based test
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DOI：
10.1093/jjfinec/nbq025
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
B. Candelon;Gilbert Colletaz;Christophe Hurlin;S. Tokpavi
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文献类型：
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作者：
B. Candelon;Gilbert Colletaz;Christophe Hurlin;S. Tokpavi
本文提出了一种新的基于持续时间的VaR预测回测方法。Bontemps（2006）提出的用于检验分布假设（即几何分布）的GMM检验框架被应用于VaR预测有效性的情况。使用简单的J-统计量的基础上定义的正交多项式与几何分布的时刻，这种新的方法解决了大多数的缺点通常与基于持续时间的回测程序。首先，它的实施非常容易。第二，它允许对无条件覆盖、独立性和条件覆盖假设进行单独的检验（Christoffersen，1998）。第三，提高了测试的可行性。第四，蒙特-卡罗模拟表明，对于现实的样本量，我们的GMM测试优于传统的基于持续时间的测试。纳斯达克回报率的实证应用证实，使用GMM测试会导致监管当局事后评估风险的重大后果。毫无疑问，本文为基于持续期检验的VaR预测的实证应用提供了强有力的支持。(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This摘要是从本条目的另一个版本中借用的。）(This abstract w（本摘要是从本条目的另一个版本中借用的。）
This paper proposes a new duration-based backtesting procedure for VaR forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e. the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple J-statistic based on the moments defined by the orthonormal polynomials associated with the geometric distribution, this new approach tackles most of the drawbacks usually associated to duration based backtesting procedures. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypothesis (Christoffersen, 1998). Third, feasibility of the tests is improved. Fourth, Monte-Carlo simulations show that for realistic sample sizes, our GMM test outperforms traditional duration based test. An empirical application for Nasdaq returns confirms that using GMM test leads to major consequences for the ex-post evaluation of the risk by regulation authorities. Without any doubt, this paper provides a strong support for the empirical application of duration-based tests for VaR forecasts. (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.) (This abstract w (This abstract was borrowed from another version of this item.)