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摘要翻译：
长期以来，对冲基金一直被视为名副其实的投资“黑匣子”，因为外人可能永远不会看到投资组合的确切构成。因此，估计一组信息丰富的资产权重的能力对于分析是非常可取的。本文提出了一个组合状态空间模型，用于估计投资组合在一组候选资产上的未观测资产配置权重，当唯一的观测信息是投资组合收益和候选资产收益的时间序列时。在本文中，我们展示了序列蒙特卡罗数值方法和条件正规分析方法来求解未观测资产权重时间序列的估计。这一方法的动机是估计每月资产类别权重的总体对冲基金行业从1996年到2012年。此外，我们还展示了如何将结果作为预测性投资权重来构建对冲基金复制投资组合。
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英文标题：
《The Dirichlet Portfolio Model: Uncovering the Hidden Composition of
Hedge Fund Investments》
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作者：
Laszlo F. Korsos
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最新提交年份：
2013
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Hedge funds have long been viewed as a veritable "black box" of investing since outsiders may never view the exact composition of portfolio holdings. Therefore, the ability to estimate an informative set of asset weights is highly desirable for analysis. We present a compositional state space model for estimation of an investment portfolio's unobserved asset allocation weightings on a set of candidate assets when the only observed information is the time series of portfolio returns and the candidate asset returns. In this paper, we exhibit both sequential Monte Carlo numerical and conditionally Normal analytical approaches to solve for estimates of the unobserved asset weight time series. This methodology is motivated by the estimation of monthly asset class weights on the aggregate hedge fund industry from 1996 to 2012. Furthermore, we show how to implement the results as predictive investment weightings in order to construct hedge fund replicating portfolios.
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PDF链接：
https://arxiv.org/pdf/1306.0938
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