Len Umantsev () (Department of Management Science and Engineering, Stanford University, Stanford, CA 94305-4026) Victor Chernozhukov () (Department of Economics, MIT, Cambridge, MA 02139)
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.
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Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
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