The purpose of this paper is to test a multi-factor modelfor realized returns implied by the generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016). This model relaxes the convention that the number of risk-factors is small. We estimate this model using a new approach for identifying risk-factors. We first obtain the collection of all possible risk-factors and then provide a simultaneous test , security by security, of which risk-factors are significant for which  securities. Since the collection of risk-factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) are used. The multi-factor model is shown to have a significantly better fit than the Fama-French 5-factor model . Robustness tests are also provided .
The paper proposes a new high-dimensional algorithm, 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. Since the collection of basis assets is quite large and highly correlated, high-dimension methods are used. The AMF model along with the GIBS algorithm is shown to have a significantly better fit than the Fama-French 5-factor model .
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purpose of this paper is to test a multi-factor modelfor realized returns
paper proposes a new high-dimensional algorithm, the Groupwise Interpretable Basis Selection (GIBS) algorithm to estimate a new Adaptive Multi-Factor (AMF) asset pricing model,
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generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016). This model
recently developed Generalized Arbitrage Pricing Theory, which
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estimate this model using a new approach for identifying risk-factors. We first obtain the collection of all possible risk-factors and then provide a simultaneous test , security by security, of which risk-factors are significant for
first obtain an adaptive collection of basis assets and then simultaneously test
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basis assets correspond to which
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risk-factors is
basis assets is quite
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(including the LASSO and prototype clustering)
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multi-factor model
AMF model along with the GIBS algorithm
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. Robustness tests are also provided