Corporate Bond Pricing via Benchmark Combination Model

Research output: Working PapersPreprint

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Original languageEnglish
PublisherSocial Science Research Network (SSRN)
Publication statusOnline published - 12 Oct 2021


This paper develops a benchmark combination model (BCM), which combines univariate-sorted basis portfolios using multiple characteristics, to perform asset evaluation. Under the non-arbitrage restriction, we estimate the common combination weights of BCM to minimize aggregate realized pricing errors. Using a long-span (45-year) sample of U.S. corporate bonds, we find BCM substantially outperforms standard factor models in pricing individual corporate bonds. BCM identifies important risk premia components, including credit rating, maturity, short-term reversal, momentum and variance. There is strong evidence of return predictability for these characteristic-sorted basis portfolios as well as high out-of-sample predictive power for individual bond returns.

Research Area(s)

  • Characteristic-based benchmark, corporate bond pricing, forecast combination, machine learning, risk premia

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Corporate Bond Pricing via Benchmark Combination Model. / He, Xin; Feng, Guanhao; Wang, Junbo et al.
Social Science Research Network (SSRN), 2021.

Research output: Working PapersPreprint