Abstract
This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeling is cast in terms of the quantile regression function — the inverse of the conditional distribution function. A basic specification analysis relates its functional forms to the benchmark models of returns and asset pricing. We stress important aspects of measuring the extremal and intermediate conditional risk. An empirical application characterizes the key economic determinants of various levels of conditional risk.
We thank Takeshi Amemiya, Herman Bierens, Emily Gallagher, Roger Koenker, Mary Ann Lawrence, Tom MaCurdy, Warren Huang, an anonymous referee, and participants of seminars at Stanford University, University of Mannheim, Midwest Finance Association Risk Session, CIRANO, International Conference on Economic Applications of Quantile Regression for many useful conversations and/or comments. Very special thanks to Bernd Fitzenberger who provided extremely useful comments and corrections as an editor.
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Chernozhukov, V., Umantsev, L. (2002). Conditional value-at-risk: Aspects of modeling and estimation. In: Fitzenberger, B., Koenker, R., Machado, J.A.F. (eds) Economic Applications of Quantile Regression. Studies in Empirical Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-11592-3_14
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DOI: https://doi.org/10.1007/978-3-662-11592-3_14
Publisher Name: Physica, Heidelberg
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