Efficient, regression-based estimation of dynamic asset pricing models
We study regression-based estimators for beta representations of dynamic asset pricing models with affine and exponentially affine pricing kernel specifications. These estimators extend static cross-sectional asset pricing estimators to settings where prices of risk vary with observed state variables. We identify conditions under which four-stage regression-based estimators are efficient and also present alternative, closed-form linearized maximum likelihood (LML) estimators. We provide multi-stage standard errors necessary to conduct inference for asset pricing tests. In empirical applications, we find that time-varying prices of risk are pervasive, thus favoring dynamic cross-sectional asset pricing models over standard unconditional specifications.
|Date of creation:||2011|
|Date of revision:|
|Contact details of provider:|| Postal: 33 Liberty Street, New York, NY 10045-0001|
Web page: http://www.newyorkfed.org/
More information through EDIRC
|Order Information:|| Web: http://www.ny.frb.org/rmaghome/staff_rp/ Email: |
When requesting a correction, please mention this item's handle: RePEc:fip:fednsr:493. See general 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: (Amy Farber)
If references are entirely missing, you can add them using this form.