Arbitrage Pricing Theory, the Stochastic Discount Factor and Estimation of Risk Premia from Portfolios
M Pesaran () and
Ronald Smith ()
No 9001, CESifo Working Paper Series from CESifo
The arbitrage pricing theory (APT) attributes differences in expected returns to exposure to systematic risk factors, which are typically assumed to be strong. In this paper we consider two aspects of the APT. Firstly we relate the factors in the statistical factor model to a theoretically consistent set of factors defined by their conditional covariation with the stochastic discount factor (mt) used to price securities within inter-temporal asset pricing models. We show that risk premia arise from non-zero correlation of observed factors with mt; and the pricing errors arise from the correlation of the errors in the statistical factor model with mt: Secondly we compare estimates of factor risk premia using portfolios with the ones obtained using individual securities, and show that the identification conditions in terms of the strength of the factor are the same and that, in general, no clear cut ranking of the small sample bias of the two estimators is possible.
Keywords: arbitrage pricing theory; stochastic discount factor; portfolios; factor strength; identification of risk premia; two-pass regressions; Fama-MacBeth (search for similar items in EconPapers)
JEL-codes: C38 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
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:ces:ceswps:_9001
Access Statistics for this paper
More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().