| 来源类型 | Working Paper
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| 规范类型 | 报告
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| DOI | 10.3386/w23527
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| 来源ID | Working Paper 23527
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| Inference on Risk Premia in the Presence of Omitted Factors |
| Stefano Giglio; Dacheng Xiu
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| 发表日期 | 2017-06-19
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| 出版年 | 2017
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| 语种 | 英语
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| 摘要 | We propose a three-pass method to estimate the risk premia of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the true factors that drive asset prices or they are measured with error. We show that the risk premium of a factor can be identified in a linear factor model regardless of the rotation of the other control factors as long as they together span the space of true factors. Motivated by this rotation invariance result, our approach uses principal components to recover the factor space and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. Our methodology also accounts for potential measurement error in the observed factors and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity portfolios to estimate risk premia for several workhorse linear models. The estimates are robust to the choice of test portfolios within equities as well as across many asset classes. |
| 主题 | Econometrics
; Estimation Methods
; Financial Economics
; Portfolio Selection and Asset Pricing
|
| URL | https://www.nber.org/papers/w23527
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| 来源智库 | National Bureau of Economic Research (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/581200
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推荐引用方式
GB/T 7714 |
Stefano Giglio,Dacheng Xiu. Inference on Risk Premia in the Presence of Omitted Factors. 2017.
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文件名：
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w23527.pdf
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格式：
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Adobe PDF
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