Benchmarking Individual Corporate Bonds
64 Pages Posted: 12 Oct 2021 Last revised: 24 Jun 2022
Date Written: June 20, 2022
We propose a new econometric model, benchmark combination model (BCM), to estimate and decompose asset risk premia in empirical asset pricing. BCM pricing kernel is a weighted combination of the basis portfolios sorted on many asset characteristics. With a no-arbitrage objective, our approach minimizes cross-sectional pricing errors and identifies the sources of risk premia. With a 45-year sample of U.S. corporate bonds, we find that BCM outperforms prevailing factor models in pricing corporate bonds. Second, we find credit ratings, maturity, short-term reversal, momentum, and variance are primary sources of bond risk premia. Finally, incorporating machine learning forecasts into BCM shows strong evidence of return predictability.
Keywords: Characteristic-based benchmark, high-dimensional sort, corporate bond risk premia, forecast combination, machine learning, return predictability.
JEL Classification: C1, G1
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