Results 1 to 10 of about 38,439 (134)
Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors [PDF]
Reliability Engineering and System Safety, 2019 Abstract As an important degradation model for monotonic degradation processes, the inverse Gaussian (IG) process model has attracted a lot of attention. To characterize random effects among test samples, the traditional IG process model usually assumes a normal distributed degradation rate.
Songhua Hao, Christophe Berenguer
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A Bivariate Normal Inverse Gaussian Process with Stochastic Delay: Efficient Simulations and Applications to Energy Markets [PDF]
Applied Mathematical Finance, 2021 26 pages, 4 figures, 13 ...
Matteo Gardini +2 more
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Local asymptotic normality for normal inverse Gaussian Lévy processes with high-frequency sampling [PDF]
ESAIM: Probability and Statistics, 2012 We prove the local asymptotic normality for the full parameters of the normal inverse Gaussian Levy process X, when we observe high-frequency data XΔn,X2Δn,...,XnΔn with sampling mesh Δn → 0 and the terminal sampling time nΔn → ∞. The rate of convergence turns out to be (√nΔn, √nΔn, √n, √n) for the dominating parameter (α,β,δ,μ), where α stands for the
Kawai, Reiichiro, Masuda, Hiroki
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Absolute Moments of Generalized Hyperbolic Distributions and Approximate Scaling of Normal Inverse Gaussian Lévy Processes [PDF]
Scandinavian Journal of Statistics, 2005 Abstract. Expressions for (absolute) moments of generalized hyperbolic and normal inverse Gaussian (NIG) laws are given in terms of moments of the corresponding symmetric laws. For the (absolute) moments centred at the location parameter μ explicit expressions as series containing Bessel functions are provided.
Barndorff-Nielsen, Ole Eiler +1 more
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Communications on Stochastic Analysis, 2013 Summary: We obtain explicit formulas for distributions of extrema of exponentials of time-changed Brownian motions with drift which generalize normal inverse Gaussian processes. The generalization is made by multiplying the normal inverse Gaussian processes by a constant. The results are established with respect to the equivalent martingale measure. As
R. Ivanov
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A Monte Carlo Approach to Bitcoin Price Prediction with Fractional Ornstein–Uhlenbeck Lévy Process
Forecasting, 2022 Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price ...
Jules Clément Mba +2 more
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Fractional Normal Inverse Gaussian Process [PDF]
Methodology and Computing in Applied Probability, 2010 Normal inverse Gaussian (NIG) process was introduced by Barndorff-Nielsen (1997) by subordinating Brownian motion with drift to an inverse Gaussian process. Increments of NIG process are independent and stationary. In this paper, we introduce dependence between the increments of NIG process, by subordinating fractional Brownian motion to an inverse ...
KUMAR, A, VELLAISAMY, P
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Astronomy & Astrophysics, 2023 In the solar wind (SW), the particle distribution functions are generally not Gaussian. They present nonthermal features that are related to underlying acceleration and heating processes.
P. Louarn +29 more
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Financial Return Distributions: Past, Present, and COVID-19
Entropy, 2021 We analyze the price return distributions of currency exchange rates, cryptocurrencies, and contracts for differences (CFDs) representing stock indices, stock shares, and commodities.
Marcin Wątorek +2 more
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ARMA–GARCH model with fractional generalized hyperbolic innovations
Financial Innovation, 2022 In this study, a multivariate ARMA–GARCH model with fractional generalized hyperbolic innovations exhibiting fat-tail, volatility clustering, and long-range dependence properties is introduced. To define the fractional generalized hyperbolic process, the
Sung Ik Kim
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