Single-index-based CoVaR with very high-dimensional covariates
| Year of publication: |
Apri 2018
|
|---|---|
| Authors: | Fan, Yan ; Härdle, Wolfgang ; Wang, Weining ; Zhu, Lixing |
| Published in: |
Journal of business & economic statistics : JBES ; a publication of the American Statistical Association. - Alexandria, Va. : American Statistical Association, ISSN 0735-0015, ZDB-ID 876122-X. - Vol. 36.2018, 2, p. 212-226
|
| Subject: | Composite quasi-maximum likelihood estimation | CoVaR | Lasso | Minimum average contrast estimation | Model selection | Quantile single-index regression | Schätztheorie | Estimation theory | Regressionsanalyse | Regression analysis | Maximum-Likelihood-Schätzung | Maximum likelihood estimation | Risikomaß | Risk measure |
-
TENET : Tail-Event driven NETwork risk
Härdle, Wolfgang, (2016)
-
TENET : Tail-Event driven NETwork risk
Härdle, Wolfgang, (2014)
-
Composite Quantile Regression for the Single-Index Model
Fan, Yan, (2013)
- More ...
-
Composite Quantile Regression for the Single-Index Model
Fan, Yan, (2017)
-
Composite quantile regression for the single-index model
Fan, Yan, (2013)
-
The common and specific components of inflation expectations across European countries
Chen, Shi, (2022)
- More ...