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

Liao Zhu, Sumanta Basu, Robert Jarrow () and Martin T. Wells

Papers from arXiv.org

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.

Date: 2018-04, Revised 2021-12
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Published in The Quarterly Journal of Finance. Vol. 10, No. 04, 2050017 (2020)

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Journal Article: High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1804.08472

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