Journal
MATHEMATICS
Volume 9, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/math9091011
Keywords
asset pricing; bootstrap; common principal component analysis; cross-sectional regression; factor models; time series
Categories
Funding
- V Regional Plan for Scientific Research and Technological Innovation 2016-2020 of the Community of Madrid
- Universidad Carlos III de Madrid in the action of Excellence for University Professors
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In this paper, a procedure to obtain and test multifactor models based on statistical and financial factors is proposed, utilizing dimensionality reduction technique and block-bootstrap methodology to address factor selection and parameter significance issues. Results show that the multifactor model improves the Capital Asset Pricing Model in time-series regressions.
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-R-2 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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