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DTSTART;TZID=Europe/Helsinki:20260923T124500
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DTSTAMP:20260722T081947Z
CREATED:20260714T121528Z
LAST-MODIFIED:20260722T081947Z
UID:10792-1790167500-1790172000@economia.uc3m.es
SUMMARY:A Non-Crossing Quantile and Expected Shortfall Regression
DESCRIPTION:Timo Dimitriadis\n(University Frankfurt)\n«A Non-Crossing Quantile and Expected Shortfall Regression»\nAbstract:\nRecently proposed joint and two-step estimators for Expected Shortfall (ES) and Value-at-Risk (VaR) regressions often exhibit crossings of the estimated regression functions in finite samples\, analogous to the classical quantile crossing problem. We propose an M-estimator for joint VaR and ES regression at multiple probability levels that prevents such crossings by incorporating non-crossing constraints into the optimization problem. We establish that\, when crossings arise from finite-sample variability or mild model misspecification\, the constrained estimator is asymptotically equivalent to its unconstrained counterpart\, a result corroborated by simulation studies. The constraints can be imposed over either a subset or a superset of the covariate space\, with the chosen domain acting as a regularization parameter that shrinks the regression functions toward common slopes. We illustrate the practical utility of the proposed method in two applications. First\, in recently proposed macroeconomic regressions for Inflation-at-Risk\, the non-crossing estimator not only eliminates crossings but also stabilizes parameter estimates and produces smoother predictions. Second\, for forecasting VaR and ES of global financial indices using CAViaR-type models\, imposing non-crossing constraints improves out-of-sample forecast performance compared to unconstrained CAViaR as well as classical benchmark models.\n  \n  \nFace to Face 15.2.71  –  Room 15.1.39
URL:https://economia.uc3m.es/event/tba-194/
LOCATION:15.1.39
CATEGORIES:Seminario de Econometría
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