Discussion of ‘On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures’

DOI10.1080/24754269.2020.1862587OpenAlexW3120431085MaRDI QIDQ5880057

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Publication date: 7 March 2023

Published in: Statistical Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/24754269.2020.1862587

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Rejoinder of “On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures”

Cites Work

• The extremogram: a correlogram for extreme events
• Bivariate extreme statistics. I
• Limit theorems for empirical processes of cluster functionals
• The estimation of M4 processes with geometric moving patterns
• Moving-maximum models for extrema of time series
• Inference on the tail process with application to financial time series modeling
• Sparse moving maxima models for tail dependence in multivariate financial time series
• Extremal quantile regression
• Quotient correlation: a sample based alternative to Pearson's correlation
• Random threshold driven tail dependence measures with application to precipitation data analysis
• Basic properties and prediction of max-ARMA processes
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• A structural break test for extremal dependence in β-mixing random vectors

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