Learning about Term Structure Predictability under Uncertainty

Shuo Cao

No GRU_2018_006, GRU Working Paper Series from City University of Hong Kong, Department of Economics and Finance, Global Research Unit

Abstract: This paper proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Keywords: Affine Term Structure Models; Learning; Parameter Uncertainty; Model Uncertainty; Ambiguity Aversion; Bayesian Methods (search for similar items in EconPapers)
JEL-codes: C1 C3 C5 D8 E4 G1 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2018-06-01
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cb.cityu.edu.hk/ef/doc/GRU/WPS/GRU%232018-006%20Shuo.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cth:wpaper:gru_2018_006

Access Statistics for this paper

More papers in GRU Working Paper Series from City University of Hong Kong, Department of Economics and Finance, Global Research Unit Contact information at EDIRC.
Bibliographic data for series maintained by GRU ( this e-mail address is bad, please contact ).