A multi-dynamic-factor model for stock returns
dc.contributor.author | Ng, Victor K. | en_US |
dc.contributor.author | Engle, Robert F. | en_US |
dc.contributor.author | Rothschild, Michael | en_US |
dc.date.accessioned | 2006-04-10T15:16:34Z | |
dc.date.available | 2006-04-10T15:16:34Z | |
dc.date.issued | 1992 | en_US |
dc.identifier.citation | Ng, Victor, Engle, Robert F., Rothschild, Michael (1992)."A multi-dynamic-factor model for stock returns." Journal of Econometrics 52(1-2): 245-266. <http://hdl.handle.net/2027.42/30125> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6VC0-458298B-15/2/35d362de16c206e4214d3afd513447f9 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/30125 | |
dc.description.abstract | In this paper, we define dynamic and static factors and distinguish between the dynamic and static structure of asset excess returns. We examine the value-weighted market portfolio as a dynamic factor and propose an intuitively appealing procedure to search for more dynamic factors. We find evidence that the market is a dynamic factor but a three-dynamic-factor model is superior in modelling the decile portfolios. The two additional factors are correlated with a January dummy and Bond risk premium and with production growth and a recession dummy, respectively. We found that small firms are more sensitive to the January/Bond risk factor, while large firms are more sensitive to the Production/Recession factor. We found that after accounting for the systematic risk corresponding to the three dynamic factors, there is not much of a static component of asset risk premium and there is no evidence for a higher `unexplained' return on small firm portfolios. | en_US |
dc.format.extent | 1349073 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | A multi-dynamic-factor model for stock returns | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | University of Michigan, Ann Arbor, MI 48109-1234, USA | en_US |
dc.contributor.affiliationother | University of California, San Diego, La Jolla, CA 92093-0508, USA | en_US |
dc.contributor.affiliationother | University of California, San Diego, La Jolla, CA 92093-0508, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/30125/1/0000501.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0304-4076(92)90072-Y | en_US |
dc.identifier.source | Journal of Econometrics | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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