A Combined Approach to the Inference of Conditional Factor Models
Liangjun Su and
Journal of Business & Economic Statistics, 2015, vol. 33, issue 2, 203-220
This article develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature--the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is correctly specified. Simulations indicate that our estimates and tests perform well in finite samples. In our empirical analysis, we use our new method to examine the performance of the conditional capital asset pricing model (CAPM), which has generated controversial results in the recent asset-pricing literature.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
Working Paper: A Combined Approach to the Inference of Conditional Factor Models (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:33:y:2015:i:2:p:203-220
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().