Can Statistical Models of Stock Returns "Explain" Empirical Regularities?

Phoebe Koundouri (pkoundouri@aueb.gr), Nikolaos Kourogenis and Nikitas Pittis

MPRA Paper from University Library of Munich, Germany

Abstract: Statistical models are usually thought of as means for describing statistical regularities. Concerning stock returns, many empirical regularities have been documented in the literature together with their corresponding models. The main task of this paper is to investigate, under the prism of the philosophy of science, the conditions that a statistical model has to satisfy in order to be deemed as explanatory adequate for the existing regularities. We distinguish two alternative sets of criteria for the explanatory adequacy of a statistical model. The first one is given by the Deductive-Statistical model of explanation, put forward by Hempel (1962). The second set, which contains much stricter conditions than the first, corresponds to the Deductive-Probabilistic-Nomological model suggested by Railton (1978). It is shown that the two most important statistical models of stock returns, namely the multivariate GARCH model and the Factor Model with persistent betas, are D-S explanatory. It is also shown that the Factor Model partially satisfies the D-N-P conditions for explanatory adequacy whereas the GARCH model fails completely in this respect.

Keywords: empirical regularities; stock returns; single factor model; autoregressive beta; statistical explanation (search for similar items in EconPapers)
JEL-codes: C1 Y1 (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/122492/1/MPRA_paper_122492.pdf original version (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:pra:mprapa:122492

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter (winter@lmu.de).