Joint inference on extreme expectiles for multivariate heavy-tailed distributions
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Publication:2137005
DOI10.3150/21-BEJ1375OpenAlexW3042258009MaRDI QIDQ2137005
Gilles Stupfler, Simone A. Padoan
Publication date: 16 May 2022
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.08944
Related Items (3)
Joint inference on extreme expectiles for multivariate heavy-tailed distributions ⋮ A modeler's guide to extreme value software ⋮ Optimal weighted pooling for inference about the tail index and extreme quantiles
Uses Software
Cites Work
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- Extreme Dependence Models
- Making and Evaluating Point Forecasts
- Extreme quantile estimation for dependent data, with applications to finance
- Adapting extreme value statistics to financial time series: dealing with bias and serial dependence
- Asymmetric Least Squares Estimation and Testing
- Geoadditive expectile regression
- Kernel estimators for the second order parameter in extreme value statistics
- A note on the asymptotic variance at optimal levels of a bias-corrected Hill estimator
- Adaptive estimates of parameters of regular variation
- A simple general approach to inference about the tail of a distribution
- Best attainable rates of convergence for estimators of the stable tail dependence function
- Extreme M-quantiles as risk measures: from \(L^{1}\) to \(L^{p}\) optimization
- Optimal rates of convergence for estimates of the extreme value index
- Generalized quantiles as risk measures
- Joint inference on extreme expectiles for multivariate heavy-tailed distributions
- Tail expectile process and risk assessment
- On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails
- Bias correction in extreme value statistics with index around zero
- Coherent Measures of Risk
- COHERENCE AND ELICITABILITY
- Dependence Modeling with Copulas
- Non-parametric Estimation of Tail Dependence
- Regression Quantiles
- Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
- Estimation of Tail Risk Based on Extreme Expectiles
- Detecting Tail Risk Differences in Multivariate Time Series
- Estimation of the Marginal Expected Shortfall: the Mean When a Related Variable is Extreme
- Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations and Forecast Rankings
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