Conditioning Information and Variance Bounds on Pricing Kernels
AbstractGallant, Hansen, and Tauchen (1990) show how to use conditioning information optimally to construct a sharper unconditional variance bound (the GHT bound) on pricing kernels. The literature predominantly resorts to a simple but suboptimal procedure that scales returns with predictive instruments and computes standard bounds using the original and scaled returns. This article provides a formal bridge between the two approaches. We propose an optimally scaled bound that coincides with the GHT bound when the first and second conditional moments are known. When these moments are misspecified, our optimally scaled bound yields a valid lower bound for the standard deviation of pricing kernels, whereas the GHT bound does not. We illustrate the behavior of the bounds using a number of linear and nonlinear models for consumption growth and bond and stock returns. We also illustrate how the optimally scaled bound can be used as a diagnostic for the specification of the first two conditional moments of asset returns. Copyright 2004, Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Studies in its journal The Review of Financial Studies.
Volume (Year): 17 (2004)
Issue (Month): 2 ()
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Other versions of this item:
- Geert Bekaert & Jun Liu, 1999. "Conditioning Information and Variance Bounds on Pricing Kernels," NBER Working Papers 6880, National Bureau of Economic Research, Inc.
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- Geert Bekaert & Robert J. Hodrick, 1991.
"Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets,"
NBER Working Papers
3790, National Bureau of Economic Research, Inc.
- Bekaert, Geert & Hodrick, Robert J, 1992. " Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets," Journal of Finance, American Finance Association, vol. 47(2), pages 467-509, June.
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