GSH Dependence Modeling with an Application to Risk Management
DOI10.1080/03610926.2012.686646zbMath1319.62207OpenAlexW2036847880MaRDI QIDQ3167840
Corrado Provasi, Paola Palmitesta
Publication date: 29 October 2012
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2012.686646
independent component analysisrisk measurescopula approachgeneralized secant hyperbolic (GSH) distribution
Estimation in multivariate analysis (62H12) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Approximations to statistical distributions (nonasymptotic) (62E17)
Uses Software
Cites Work
- Unnamed Item
- Elliptical copulas: Applicability and limitations.
- Efficiency comparisons of maximum-likelihood-based estimators in GARCH models
- Constructing multivariate distributions with specific marginal distributions
- Tests of independence and randomness based on the empirical copula process
- Aggregation of dependent risks using the Koehler-Symanowski copula function
- Coherent Measures of Risk
- The t Copula and Related Copulas
- Copula Component Analysis
- An empirical analysis of multivariate copula models
- The multivariate skew-normal distribution
- THE GENERALIZED SECANT HYPERBOLIC DISTRIBUTION AND ITS PROPERTIES
- Model Selection and Multimodel Inference
- Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
This page was built for publication: GSH Dependence Modeling with an Application to Risk Management
