ghyp

R package ghyp: A package on the generalized hyperbolic distribution and its special cases. This package provides detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution.


References in zbMATH (referenced in 29 articles )

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  1. Lee, Sharon X.; McLachlan, Geoffrey J.: An overview of skew distributions in model-based clustering (2022)
  2. Masuda, Hiroki; Mercuri, Lorenzo; Uehara, Yuma: Noise inference for ergodic Lévy driven SDE (2022)
  3. Punzo, Antonio; Bagnato, Luca: Dimension-wise scaled normal mixtures with application to finance and biometry (2022)
  4. Amiri, M.; Roozegar, R.; Jamalizadeh, A.: Nonlinear regression using order statistics from the multivariate generalized hyperbolic distributions (2021)
  5. Billio, Monica; Frattarolo, Lorenzo; Guégan, Dominique: Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case (2021)
  6. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  7. Fotopoulos, Stergios B.; Jandhyala, Venkata K.; Paparas, Alex: Some properties of the multivariate generalized hyperbolic laws (2021)
  8. Li, Zihao; Luo, Ji; Yao, Jing: Convex bound approximations for sums of random variables under multivariate log-generalized hyperbolic distribution and asymptotic equivalences (2021)
  9. Punzo, Antonio; Bagnato, Luca: The multivariate tail-inflated normal distribution and its application in finance (2021)
  10. Nitithumbundit, Thanakorn; Chan, Jennifer S. K.: ECM algorithm for auto-regressive multivariate skewed variance gamma model with unbounded density (2020)
  11. Shiraya, Kenichiro; Uenishi, Hiroki; Yamazaki, Akira: A general control variate method for Lévy models in finance (2020)
  12. Wozabal, David; Rameseder, Gunther: Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity (2020)
  13. Murray, Paula M.; Browne, Ryan P.; McNicholas, Paul D.: Note of clarification on “Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering” (2019)
  14. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  15. Villa, Cristiano; Rubio, Francisco J.: Objective priors for the number of degrees of freedom of a multivariate (t) distribution and the (t)-copula (2018)
  16. Yoshiba, Toshinao: Maximum likelihood estimation of skew-(t) copulas with its applications to stock returns (2018)
  17. Das, Sourish; Halder, Aritra; Dey, Dipak K.: Regularizing portfolio risk analysis: a Bayesian approach (2017)
  18. Mattei, Pierre-Alexandre: Multiplying a Gaussian matrix by a Gaussian vector (2017)
  19. Yu, Yaming: On normal variance-mean mixtures (2017)
  20. Chan, Stephen; Nadarajah, Saralees; Afuecheta, Emmanuel: An \textttRpackage for value at risk and expected shortfall (2016)

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