Prediction of extremal expectile based on regression models with heteroscedastic extremes
Year of publication: |
2022
|
---|---|
Authors: | Xu, Wen ; Hou, Yanxi ; Li, Deyuan |
Published in: |
Journal of business & economic statistics : JBES ; a publication of the American Statistical Association. - Abingdon : Taylor & Francis, ISSN 1537-2707, ZDB-ID 2043744-4. - Vol. 40.2022, 2, p. 522-536
|
Subject: | Expectile regression | Heteroscedastic extremes | Quantile regression | Tail risk | Regressionsanalyse | Regression analysis | Schätztheorie | Estimation theory | Risikomaß | Risk measure | Ausreißer | Outliers | Statistische Verteilung | Statistical distribution |
Description of contents: | Description [tandfonline.com] |
-
Estimation of extreme value-at-risk : an EVT approach for quantile GARCH model
Yi, Yanping, (2014)
-
Estimation of high conditional tail risk based on expectile regression
Hu, Jie, (2021)
-
Efficient estimation in extreme value regression models of hedge fund tail risks
Hambuckers, Julien, (2023)
- More ...
-
Dong, Yun, (2021)
-
Volatility forecasting using intraday information with the CARR models for the China stock markets
Wu, Chun-Chou, (2023)
-
The Impact of competition on prices with numerous firms
Gabaix, Xavier, (2013)
- More ...