摘要翻译：
本文提出了一种新的计算风险价值(VaR)的方法，用于量化信贷组合中的损失。我们用Haar小波基函数的有限组合来逼近损失函数的累积分布，并通过拉普拉斯变换的反演来计算逼近系数。实际上，我们证明了只需要几个近似系数，就可以快速地得到VaR。为了检验该方法，我们将Vasicek单因素投资组合信用损失模型作为我们的模型框架。Haar小波方法在处理规模较小或集中的投资组合时具有快速、准确和鲁棒性，当违反Basel II公式的假设时。
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英文标题：
《Haar Wavelets-Based Approach for Quantifying Credit Portfolio Losses》
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作者：
Josep J. Masdemont, Luis Ortiz-Gracia
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最新提交年份：
2009
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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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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
  This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelets basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. In fact, we demonstrate that only a few coefficients of the approximation are needed, so VaR can be reached quickly. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. The Haar wavelets method is fast, accurate and robust to deal with small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated.
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PDF链接：
https://arxiv.org/pdf/0904.4620