Conditional stochastic dominance tests in dynamic settings
This paper proposes nonparametric consistent tests of conditional stochastic dominance of arbitrary order in a dynamic setting. The novelty of these tests lies in the nonparametric manner of incorporating the information set into the test. The test allows for general forms of unknown serial and mutual dependence between random variables, and has an asymptotic distribution that can be easily approximated by simulation. This method has good finite-sample performance. These tests are applied to determine the investment efficiency between U S industry portfolios conditional on the dynamics of the market portfolio. The empirical analysis suggests that Telecommunications dominates the other sectoral portfolios under risk aversion.
|Date of creation:||Feb 2012|
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- Scaillet, Olivier & Topaloglou, Nikolas, 2010.
"Testing for Stochastic Dominance Efficiency,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 28(1), pages 169-180.
- Olivier Scaillet & Nikolas Topaloglou, 2005. "Testing for Stochastic Dominance Efficiency," FAME Research Paper Series rp154, International Center for Financial Asset Management and Engineering.
- Nikolas Topaloglou & Olivier Scaillet & University of Geneva, 2006. "Testing foe Stochastic Dominance Efficiency," Computing in Economics and Finance 2006 74, Society for Computational Economics.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
- Miguel A. Delgado & Juan Carlos Escanciano, 2011.
"Conditional stochastic dominance testing,"
Economics Working Papers
we1138, Universidad Carlos III, Departamento de Economía.
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- Garry F. Barrett & Stephen G. Donald, 2003. "Consistent Tests for Stochastic Dominance," Econometrica, Econometric Society, vol. 71(1), pages 71-104, January.
- Delgado, Miguel A. & Carlos Escanciano, J., 2007. "Nonparametric tests for conditional symmetry in dynamic models," Journal of Econometrics, Elsevier, vol. 141(2), pages 652-682, December.
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