High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

Liao Zhu, Sumanta Basu, Robert Jarrow () and Martin T. Wells
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Liao Zhu: Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA
Sumanta Basu: Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA
Martin T. Wells: Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA

Quarterly Journal of Finance (QJF), 2020, vol. 10, issue 04, 1-52

Abstract: The paper proposes a new algorithm for the high-dimensional financial data — the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama–French 5-factor model.

Keywords: Asset pricing models; AMF model; GIBS algorithm; high-dimensional statistics; machine learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Working Paper: High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:qjfxxx:v:10:y:2020:i:04:n:s2010139220500172

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DOI: 10.1142/S2010139220500172

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