Misspecification-Robust Inference in Linear Asset Pricing Models with Irrelevant Risk Factors

87 Pages Posted: 22 Mar 2015

See all articles by Nikolay Gospodinov

Nikolay Gospodinov

Federal Reserve Bank of Atlanta

Raymond Kan

University of Toronto - Rotman School of Management

Cesare Robotti

Imperial College Business School

Date Written: October 2013

Abstract

We show that in misspecified models with useless factors (for example, factors that are independent of the returns on the test assets), the standard inference procedures tend to erroneously conclude, with high probability, that these irrelevant factors are priced and the restrictions of the model hold. Our proposed model selection procedure, which is robust to useless factors and potential model misspecification, restores the standard inference and proves to be effective in eliminating factors that do not improve the model's pricing ability. The practical relevance of our analysis is illustrated using simulations and empirical applications.

Keywords: asset pricing models, lack of identification, model misspecification, GMM estimation

JEL Classification: G12, C12, C52

Suggested Citation

Gospodinov, Nikolay and Kan, Raymond and Robotti, Cesare, Misspecification-Robust Inference in Linear Asset Pricing Models with Irrelevant Risk Factors (October 2013). FRB Atlanta Working Paper No. 2013-9; Rotman School of Management Working Paper No. 2579821. Available at SSRN: https://ssrn.com/abstract=2579821 or http://dx.doi.org/10.2139/ssrn.2579821

Nikolay Gospodinov

Federal Reserve Bank of Atlanta ( email )

Atlanta, GA 30309
United States

HOME PAGE: https://www.frbatlanta.org/research/economists/gospodinov-nikolay.aspx?panel=1

Raymond Kan

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada
416-978-4291 (Phone)
416-971-3048 (Fax)

Cesare Robotti (Contact Author)

Imperial College Business School ( email )

South Kensington Campus
Exhibition Road
London SW7 2AZ, SW7 2AZ
United Kingdom

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