英文标题：
《Russian-Doll Risk Models》
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作者：
Zura Kakushadze
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最新提交年份：
2017
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英文摘要：
We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested \"Russian-doll\" embedding: the factor covariance matrix itself is modeled via a factor model, whose factor covariance matrix in turn is modeled via a factor model, and so on. We discuss in detail how to implement this algorithm in the case of (binary) industry classification based risk factors (e.g., \"sector -> industry -> sub-industry\"), and also in the presence of (non-binary) style factors. Our algorithm is particularly useful when long historical lookbacks are unavailable or undesirable, e.g., in short-horizon quant trading.
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中文摘要：
我们给出了一个建立多因素风险模型的简单显式算法。它大大减少或完全消除了需要计算因子协方差矩阵的风险因子的数量。这是通过嵌套的“俄罗斯玩偶”嵌入实现的：因子协方差矩阵本身通过因子模型建模，因子协方差矩阵又通过因子模型建模，以此类推。我们详细讨论了如何在基于（二元）行业分类的风险因素（例如，“行业->行业->子行业”）以及（非二元）类型因素的情况下实现该算法。当长期历史回溯不可用或不受欢迎时，我们的算法特别有用，例如在短期定量交易中。
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分类信息：

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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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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