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 22 articles )

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  1. Fotopoulos, Stergios B.; Jandhyala, Venkata K.; Paparas, Alex: Some properties of the multivariate generalized hyperbolic laws (2021)
  2. Li, Zihao; Luo, Ji; Yao, Jing: Convex bound approximations for sums of random variables under multivariate log-generalized hyperbolic distribution and asymptotic equivalences (2021)
  3. Nitithumbundit, Thanakorn; Chan, Jennifer S. K.: ECM algorithm for auto-regressive multivariate skewed variance gamma model with unbounded density (2020)
  4. Shiraya, Kenichiro; Uenishi, Hiroki; Yamazaki, Akira: A general control variate method for Lévy models in finance (2020)
  5. Wozabal, David; Rameseder, Gunther: Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity (2020)
  6. 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)
  7. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  8. Villa, Cristiano; Rubio, Francisco J.: Objective priors for the number of degrees of freedom of a multivariate (t) distribution and the (t)-copula (2018)
  9. Yoshiba, Toshinao: Maximum likelihood estimation of skew-(t) copulas with its applications to stock returns (2018)
  10. Das, Sourish; Halder, Aritra; Dey, Dipak K.: Regularizing portfolio risk analysis: a Bayesian approach (2017)
  11. Mattei, Pierre-Alexandre: Multiplying a Gaussian matrix by a Gaussian vector (2017)
  12. Yu, Yaming: On normal variance-mean mixtures (2017)
  13. Chan, Stephen; Nadarajah, Saralees; Afuecheta, Emmanuel: An \textttRpackage for value at risk and expected shortfall (2016)
  14. Fabrizi, Enrico; Trivisano, Carlo: Bayesian conditional mean estimation in log-normal linear regression models with finite quadratic expected loss (2016)
  15. Veraart, Almut E. D.: Modelling the impact of wind power production on electricity prices by regime-switching Lévy semistationary processes (2016)
  16. Arslan, Olcay: Variance-mean mixture of the multivariate skew normal distribution (2015)
  17. Devroye, Luc: Random variate generation for the generalized inverse Gaussian distribution (2014)
  18. Jakob, Kevin; Fischer, Matthias: Quantifying the impact of different copulas in a generalized CreditRisk(^+) framework. An empirical study (2014)
  19. Barndorff-Nielsen, Ole E.; Benth, Fred Espen; Veraart, Almut E. D.: Modelling energy spot prices by volatility modulated Lévy-driven Volterra processes (2013)
  20. Dingeç, Kemal Dinçer; Hörmann, Wolfgang: A general control variate method for option pricing under Lévy processes (2012)

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