A Markov Model of Heteroskedasticity, Risk, and Learning in the Stock Market
AbstractRisk premia in the stock market are assumed to move with time varying risk. We present a model in which the variance of time excess return of a portfolio depends on a state variable generated by a first-order Markov process. A model in which the realization of the state is known to economic agents, but unknown to the econometrician. is estimated. The parameter estimates are found to imply that time risk premium declines as time variance of returns rises. We then extend the model to allow agents to be uncertain about time state. Agents make their decisions in period t using a prior distribution of time state based only on past realizations of the excess return through period t-1 plus knowledge of the structure of the model. These parameter estimates from this model are consistent with asset pricing theory.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 2818.
Date of creation: Jan 1989
Date of revision:
Publication status: published as Journal of Financial Economics, April 1990.
Contact details of provider:
Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
Web page: http://www.nber.org
More information through EDIRC
Other versions of this item:
- Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.