Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks
By Victor Chernozhukov, Iv'an Fern'andez-Val. 2009
64 citations recorded by the source corpus.
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.
Subfields: Economics and Econometrics, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/144e66900123057065c8b5a0dfe21171d57b6bf1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Iv'an Fern'andez-Val
- Victor Chernozhukov
- Tetsuya Kaji
- Christian Hansen
- Martin P. Weidner
- Stefan Wager
- Guido W. Imbens
- Blaise Melly
In Finance
- Tetsuya Kaji
- Iv'an Fern'andez-Val
- Victor Chernozhukov
- Roger W. Koenker
- Alex J. Cannon
- Jozef Baruník
- Filip Žikeš
- M. Arellano
In Statistics and Probability
- Iv'an Fern'andez-Val
- Victor Chernozhukov
- Tetsuya Kaji
- Christian Hansen
- Stefan Wager
- Alexandre Belloni
- Kengo Kato
- D. Chetverikov
Citations in the corpus
- Extremal quantile regression: An overview (2016, methodology): “Chernozhukov and Fern´andez-Val (2011) developed an extremal subsampling method to estimate the distributions of ˆ Z T ( k T ) and Z T ( k T ).”
- VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles (2015, unknown): “3 An extensive discussion on how to properly use quantile regression to estimate VaR can be found in Chernozhukov and Umantsev (2001) in which they also emphasize the importance of using extremal or near-extremal quantile regression.”