Conditioning information and variance bounds on pricing kernels

• 成果类型: Article
• 署名作者: Bekaert, G; Liu, J
• 署名单位: Columbia University; National Bureau of Economic Research; University of California System; University of California Los Angeles
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhg052
• 发表日期: 2004
• 页码: 339
• 关键词: INTERTEMPORAL MARGINAL RATES; FOREIGN-EXCHANGE MARKETS; asset returns; Portfolio performance; EXCESS RETURNS; risk premia; volatility; models; restrictions; consumption

摘要

Gallant, 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.