英文标题：
《Spectral backtests of forecast distributions with application to risk
management》
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作者：
Michael B. Gordy and Alexander J. McNeil
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最新提交年份：
2019
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英文摘要：
We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which makes explicit the user\'s priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and further propose novel variants which are easily implemented, well-sized and have good power. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.
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中文摘要：
我们研究了一类预测分布的后验检验，其中检验统计量依赖于一种谱变换，该变换通过建模概率水平的函数对超标事件进行加权。权重方案由内核度量指定，该度量明确了用户对模型性能的优先级。光谱反测试包括无条件覆盖测试和条件覆盖测试。我们展示了该类如何在现有文献中嵌入各种各样的回溯测试，并进一步提出了易于实现、规模良好且功能强大的新变体。在实证应用中，我们对十家银行交易组合的隔夜损益预测分布进行了回溯测试。对于某些投资组合，测试结果在很大程度上取决于内核的选择。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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