G2TT
来源类型Discussion paper
规范类型论文
来源IDDP12926
DP12926 Estimating Latent Asset-Pricing Factors
Martin Lettau
发表日期2018-05-10
出版年2018
语种英语
摘要We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with significantly smaller pricing errors.
主题Financial Economics
关键词Cross section of returns Anomalies Expected returns High-dimensional data Latent factors Weak factors Pca
URLhttps://cepr.org/publications/dp12926
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/541738
推荐引用方式
GB/T 7714
Martin Lettau. DP12926 Estimating Latent Asset-Pricing Factors. 2018.
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