Backtesting Value-at-Risk: A GMM Duration-Based Test
提出一种新的基于持续时间的回溯测试方法，用于检验风险价值（VaR）预测的有效性，通过GMM检验框架克服传统方法的缺陷，并应用于纳斯达克收益率数据。
This paper proposes a new duration-based backtesting procedure for value-at-risk (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. An empirical application for Nasdaq returns confirms that using GMM test leads to major consequences for the expost evaluation of the risk by regulation authorities.