英文标题：
《Asset Allocation Strategies Based on Penalized Quantile Regression》
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作者：
Giovanni Bonaccolto, Massimiliano Caporin and Sandra Paterlini
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最新提交年份：
2015
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英文摘要：
It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.
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中文摘要：
众所周知，只要关注响应变量左分位数的估计，分位数回归模型就可以最小化投资组合的极端风险。我们表明，通过考虑因变量的整个条件分布，可以优化不同的风险和绩效指标。特别是，我们引入了一个经风险调整的盈利能力指标，这有助于从悲观的角度评估金融投资组合，因为回报贡献是最有利结果的净值。此外，在考虑大型投资组合时，我们还通过对资产权重引入l1范数惩罚来应对维度问题。
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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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