ESTIMATION OF HIGH CONDITIONAL TAIL RISK BASED ON EXPECTILE REGRESSION
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Publication:5152549
DOI10.1017/asb.2021.3zbMath1479.91328OpenAlexW3133201833MaRDI QIDQ5152549
Publication date: 24 September 2021
Published in: ASTIN Bulletin (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/asb.2021.3
Nonparametric regression and quantile regression (62G08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Actuarial mathematics (91G05)
Cites Work
- Unnamed Item
- Asymmetric Least Squares Estimation and Testing
- Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations
- Assessing value at risk with CARE, the conditional autoregressive expectile models
- Bias reduction for high quantiles
- A simple general approach to inference about the tail of a distribution
- A synthesis of risk measures for capital adequacy
- Generalized quantiles as risk measures
- ExpectHill estimation, extreme risk and heavy tails
- Tail expectile process and risk assessment
- Coherent Measures of Risk
- Regression Quantiles
- Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
- Asymmetric least squares regression estimation: A nonparametric approach∗
- Estimation of Tail Risk Based on Extreme Expectiles
- Estimation of High Conditional Quantiles for Heavy-Tailed Distributions
- Tail Index Regression
- A simple second-order reduced bias’ tail index estimator
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