Hierarchical Time-Varying Estimation of Asset Pricing Models

Richard T. Baillie, Fabio Calonaci and George Kapetanios
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Richard T. Baillie: King’s Business School, King’s College London, 30 Aldwych, London WC2B 4BG, UK
Fabio Calonaci: School of Economics and Finance, Queen Mary University of London, London E1 4NS, UK

JRFM, 2022, vol. 15, issue 1, 1-26

Abstract: This paper presents a new hierarchical methodology for estimating multi factor dynamic asset pricing models. The approach is loosely based on the sequential Fama–MacBeth approach and developed in a kernel regression framework. However, the methodology uses a very flexible bandwidth selection method which is able to emphasize recent data and information to derive the most appropriate estimates of risk premia and factor loadings at each point in time. The choice of bandwidths and weighting schemes are achieved by a cross-validation procedure; this leads to consistent estimators of the risk premia and factor loadings. Additionally, an out-of-sample forecasting exercise indicates that the hierarchical method leads to a statistically significant improvement in forecast loss function measures, independently of the type of factor considered.

Keywords: asset pricing model; Fama–MacBeth model; estimation of beta; kernel-weighted regressions; cross-validation; time-varying parameter regressions (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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