The Generalized Hyperbolic Model: Estimation, Financial Derivatives, and Risk Measures
By K. Prause. 1999
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- Antonis Papapantoleon
- Peter Tankov
- Jan Kallsen
- Yu Nishiyama
- Wim Schoutens
- Kenji Fukumizu
- Kathrin Glau
- L. Valdivieso
Citations in the corpus
- Characteristic Kernels and Infinitely Divisible Distributions (2014, background): “, NIGs, hyperbolic distributions, VG distributions, Laplace distributions, Cauchy distributions, and Student’s t distributions, as special cases and limiting cases (Barndorff-Nielsen and Halgreen, 1977; Prause, 1999; v. Hammerstein, 2010).”
- Which Copula is the Right One (2000, methodology): “The IFM method could not be apply to the distribution (21).”
- Lévy Processes in Finance: Theory, Numerics, and Empirical Facts (2000, background): “Eberlein and Prause (1998) and Prause (1999) finally study the whole family of generalized hyperbolic Lévy processes.”
- Risk bounds for the non-parametric estimation of Lévy processes (2006, background): “For instance, analyzing generalized hyperbolic Lévy processes, the author of [24] notices that the likelihood function is highly flat for a wide range of parameters and good starting values as well as convergence are critical.”
- ghyp: A package on generalized hyperbolic distributions (2009, unknown): “In case of the (λ, α, μ,∆, δ,β) parametrization one can define α = αδ and β = βδ (see the appendix of Prause (1999)) to get the invariant density”
- Oil price dynamics (2002-2006) (2008, unknown): “The latter is defined as a normal mean-variance distribution mixed by a generalized inverse Gaussian distribution (see Prause, 1999).”
- Generalized Hyperbolic Distributions and Brazilian Data (2004, unknown): “We refer to Barndorff-Nielsen (1978) and Prause (1999) for a detailed description.”
- Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution (2016, methodology): “The GH distribution has a wider class of distribution but the parameters of the GH distribution are difficult to estimate as pointed out by Prause (1999) and Aas and Haff (2006).”
- Maximum likelihood estimation in processes of Ornstein-Uhlenbeck type (2009, background): “Jiang and Pedersen (2003) and Prause (1999) have also addressed this problem in the context of the Barndorff-Nielsen and Shepard model with a superposition of inverse Gaussian-OU processes for the volatility coefficient.”