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Robust Inference for Consumption‐Based Asset Pricing
Author(s) -
KLEIBERGEN FRANK,
ZHAN ZHAOGUO
Publication year - 2020
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/jofi.12855
Subject(s) - capital asset pricing model , econometrics , inference , statistic , consumption (sociology) , test statistic , risk premium , macro , economics , asset (computer security) , statistical inference , consumption based capital asset pricing model , statistical hypothesis testing , statistics , computer science , mathematics , computer security , artificial intelligence , sociology , programming language , social science
The reliability of traditional asset pricing tests depends on: (i) the correlations between asset returns and factors; (ii) the time series sample size T compared to the number of assets N . For macro‐risk factors, like consumption growth, (i) and (ii) are often such that traditional tests cannot be trusted. We extend the Gibbons‐Ross‐Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, but show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.