Robust estimators under a functional common principal components model

2017 ◽  
Vol 113 ◽  
pp. 424-440 ◽  
Author(s):  
Juan Lucas Bali ◽  
Graciela Boente
Keyword(s):  
Principal Components ◽  
Robust Estimators ◽  
Common Principal Components

Common principal components (CPC)

2021 ◽  
pp. 289-324
Author(s):  
Nickolay Trendafilov ◽  
Michele Gallo
Keyword(s):  
Principal Components ◽  
Common Principal Components

scholarly journals How to Explain the Cross-Section of Equity Returns through Common Principal Components

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1011
Author(s):  
José Manuel Cueto ◽  
Aurea Grané ◽  
Ignacio Cascos
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Principal Components ◽  
Equity Returns ◽  
Block Bootstrap ◽  
Time Series Regression ◽  
Cross Sectional ◽  
Multifactor Models ◽  
Multifactor Model ◽  
Common Principal Components

In this paper, we propose a procedure to obtain and test multifactor models based on statistical and financial factors. A major issue in the factor literature is to select the factors included in the model, as well as the construction of the portfolios. We deal with this matter using a dimensionality reduction technique designed to work with several groups of data called Common Principal Components. A block-bootstrap methodology is developed to assess the validity of the model and the significance of the parameters involved. Data come from Reuters, correspond to nearly 1250 EU companies, and span from October 2009 to October 2019. We also compare our bootstrap-based inferential results with those obtained via classical testing proposals. Methods under assessment are time-series regression and cross-sectional regression. The main findings indicate that the multifactor model proposed improves the Capital Asset Pricing Model with regard to the adjusted-R2 in the time-series regressions. Cross-section regression results reveal that Market and a factor related to Momentum and mean of stocks’ returns have positive risk premia for the analyzed period. Finally, we also observe that tests based on block-bootstrap statistics are more conservative with the null than classical procedures.

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