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来源类型Working Paper
规范类型报告
DOI10.3386/w29833
来源IDWorking Paper 29833
High-Dimensional Factor Models with an Application to Mutual Fund Characteristics
Martin Lettau
发表日期2022-03-14
出版年2022
语种英语
摘要This paper considers extensions of two-dimensional factor models to higher-dimensional data represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the model using a three-dimensional data set consisting of 25 characteristics of 1,342 mutual funds observed over 34 quarters. The tensor factor models reduce the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard two-dimensional models and show that the components of the model have clear economic interpretations.
主题Econometrics ; Estimation Methods ; Financial Economics ; Portfolio Selection and Asset Pricing
URLhttps://www.nber.org/papers/w29833
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/587506
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GB/T 7714
Martin Lettau. High-Dimensional Factor Models with an Application to Mutual Fund Characteristics. 2022.
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