The Exact Distribution of the Hansen–Jagannathan Bound

Raymond Kan () and Cesare Robotti ()
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Raymond Kan: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Cesare Robotti: Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom

Management Science, 2016, vol. 62, issue 7, 1915-1943

Abstract: Under the assumption of multivariate normality of asset returns, this paper presents a geometric interpretation and the finite-sample distributions of the sample Hansen–Jagannathan bounds on the variance of admissible stochastic discount factors, with and without the nonnegativity constraint on the stochastic discount factors. In addition, since the sample Hansen–Jagannathan bounds can be very volatile, we propose a simple method to construct confidence intervals for the population Hansen–Jagannathan bounds. Finally, we show that the analytical results in the paper are robust to departures from the normality assumption.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2222 . This paper was accepted by Jerome Detemple, operations management .

Keywords: Hansen–Jagannathan bounds; finite-sample distributions; maximum likelihood estimators; in-sample arbitrage portfolios (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:62:y:2016:i:7:p:1915-1943

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