英文文献：Evaluating Value-at-Risk Models with Desk-Level Data-利用桌面级数据评估风险价值模型
英文文献作者：Peter Christoffersen,Jeremy Berkowitz,Denis Pelletier
英文文献摘要：
We present new evidence on disaggregated profit and loss (P/L) and Value-at-Risk (VaR) forecasts obtained from a large international commercial bank. Our dataset includes the actual daily P/L generated by four separate business lines within the bank. All four business lines are involved in securities trading and each is observed daily for a period of at least two years. Given this unique dataset, we provide an integrated, unifying framework for assessing the accuracy of VaR forecasts. We use a comprehensive Monte Carlo study to assess which of these many tests have the best finite-sample size and power properties. Our desk-level data set provides importance guidance for choosing realistic P/L generating processes in the Monte Carlo comparison of the various tests. The CaViaR test of Engle and Manganelli (2004) performs best overall but duration-based tests also perform well in many cases.

我们提出了从一家大型国际商业银行获得的分列损益(P/L)和风险价值(VaR)预测的新证据。我们的数据集包括银行内四个独立业务线生成的实际每日P/L。所有这四种业务都涉及证券交易，每一种业务都必须每天进行观察，为期至少两年。鉴于这个独特的数据集，我们提供了一个集成的，统一的框架，以评估VaR预测的准确性。我们使用一个全面的蒙特卡洛研究来评估这些测试中哪一个有最好的有限样本大小和功率特性。在各种测试的蒙特卡罗比较中，我们的桌面级数据集为选择现实的P/L生成过程提供了重要的指导。Engle和Manganelli(2004)的鱼子酱测试总体上表现最好，但基于时间的测试在许多情况下也表现良好。