The Geometric-VaR Backtesting Method
提出一种新的几何VaR检验方法，利用VaR违规间隔时间和VaR值来评估VaR预测的准确性，蒙特卡洛研究表明该方法对多种替代模型具有高检验功效。
This article develops a new test to evaluate value-at-risk (VaR) forecasts. VaR is a standard risk measure widely utilized by financial institutions and regulators, yet estimating VaR is a challenging problem, and popular VaR forecast relies on unrealistic assumptions. Hence, assessing the performance of VaR is of great importance. We propose the geometric-VaR test which utilizes the duration between the violations of VaR as well as the value of VaR. We conduct a Monte Carlo study based on desk-level data and we find that our test has high power against various alternatives.