A normal inverse Gaussian model for a risky asset with dependence

2012 ◽  
Vol 82 (1) ◽  
pp. 109-115 ◽  
Author(s):  
N.N. Leonenko ◽  
S. Petherick ◽  
A. Sikorskii
Keyword(s):  
Gaussian Model ◽  
Risky Asset ◽  
Inverse Gaussian ◽  
Normal Inverse Gaussian

scholarly journals A NOTE ON PORTFOLIO MANAGEMENT UNDER NON-GAUSSIAN LOGRETURNS

2001 ◽  
Vol 04 (05) ◽  
pp. 711-731 ◽  
Author(s):  
FRED ESPEN BENTH ◽  
KENNETH HVISTENDAHL KARLSEN ◽  
KRISTIN REIKVAM
Keyword(s):  
New York ◽  
Portfolio Management ◽  
Optimal Allocation ◽  
Stock Exchange ◽  
Gaussian Model ◽  
Risky Asset ◽  
Management Problem ◽  
Inverse Gaussian ◽  
Non Gaussian ◽  
Normal Inverse Gaussian

We calculate numerically the optimal allocation and consumption strategies for Merton's optimal portfolio management problem when the risky asset is modelled by a geometric normal inverse Gaussian Lévy process. We compare the computed strategies to the ones given by the standard asset model of geometric Brownian motion. To have realistic parameters in our studies, we choose Norsk Hydro quoted on the New York Stock Exchange as the risky asset. We find that an investor believing in the normal inverse Gaussian model puts a greater fraction of wealth into the risky asset. We also investigate the limiting investment rate when the volatility increases. We observe different behaviour in the two models depending on which parameters we vary in the normal inverse Gaussian distribution.

Closed-form valuations of basket options using a multivariate normal inverse Gaussian model

2009 ◽  
Vol 44 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Yang-Che Wu ◽  
Szu-Lang Liao ◽  
So-De Shyu
Keyword(s):  
Closed Form ◽  
Gaussian Model ◽  
Inverse Gaussian ◽  
Multivariate Normal ◽  
Basket Options ◽  
Normal Inverse Gaussian

Outliers Data Mining in Normal-Inverse Gaussian Model

2010 ◽  
Author(s):  
Li-li Wang ◽  
Xiang-yang Hou ◽  
Yan-ye Xiong
Keyword(s):  
Data Mining ◽  
Gaussian Model ◽  
Inverse Gaussian ◽  
Normal Inverse Gaussian

A Method for Image Denoising Based on Normal Inverse Gaussian Model Using Bayesian Estimation

2010 ◽  
Vol 30 (1) ◽  
pp. 70-74 ◽  
Author(s):  
张鑫 Zhang Xin ◽  
井西利 Jing Xili
Keyword(s):  
Bayesian Estimation ◽  
Image Denoising ◽  
Gaussian Model ◽  
Inverse Gaussian ◽  
Normal Inverse Gaussian

scholarly journals Modeling Baltic market benchmark index: a comparison of models

2019 ◽  
Vol 60 ◽  
pp. 6-10
Author(s):  
Igoris Belovas
Keyword(s):  
Maximum Likelihood Method ◽  
Gaussian Model ◽  
Likelihood Method ◽  
Ratio Test ◽  
Inverse Gaussian ◽  
Gaussian Models ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Student’S T ◽  
Normal Inverse Gaussian

In this paper we perform a statistical analysis of the returns of OMX Baltic Benchmark index. We construct symmetric α-stable, non-standardized Student’s t and normal-inverse Gaussian models of daily logarithmic returns of the index, using maximum likelihood method for the estimation of the parameters of the models. The adequacy of the modeling is evaluated with the Kolmogorov-Smirnov tests for composite hypothesis. The results of the study indicate that the normal-inverse Gaussian model outperforms alternative heavy-tailed models for long periods of time, while the non-standardized Student’s t model provides the best overall fit for the data for shorter intervals. According to the likelihood-ratio test, the four-parameter models of the log-returns of OMX Baltic Benchmark index could be reduced to the three-parameter (symmetric) models without much loss.  

Symmetric Normal Inverse Gaussian Model based Non-local Image Denoising

2012 ◽  
Vol 7 (17) ◽  
pp. 424-434
Author(s):  
Yuanjiang LI ◽  
Yuehua LI
Keyword(s):  
Image Denoising ◽  
Gaussian Model ◽  
Inverse Gaussian ◽  
Model Based ◽  
Non Local ◽  
Normal Inverse Gaussian ◽  
Local Image

scholarly journals Copula-normal inverse Gaussian model for portfolio risk assessment

2018 ◽  
Vol 29 (1) ◽  
pp. 113-148
Author(s):  
선제우 ◽  
송성주 ◽  
윤정연
Keyword(s):  
Risk Assessment ◽  
Gaussian Model ◽  
Inverse Gaussian ◽  
Portfolio Risk ◽  
Normal Inverse Gaussian

scholarly journals Existence Conditions of Super-Replication Cost in a Multinomial Model

2017 ◽  
Vol 9 (4) ◽  
pp. 185
Author(s):  
Mei Xing
Keyword(s):  
Stock Price ◽  
Gaussian Model ◽  
Time Interval ◽  
Inverse Gaussian ◽  
Multinomial Model ◽  
Liquidity Premium ◽  
Gamma Model ◽  
Existence Conditions ◽  
Variance Gamma Model ◽  
Normal Inverse Gaussian

This paper gives a theorem for the continuous time super-replication cost of European options in an unbounded multinomial market. An approximation multinomial scheme is put forward on a finite time interval [0,1] corresponding to a pure jump Lévy model with unbounded jumps. Under the assumption that the expected underlying stock price at time 1 is bounded, the limit of the sequence of the super-replication cost in a multinomial model is proved to be greater than or equal to an optimal control problem. Furthermore, it is discussed that the existence conditions of a super-replication cost and a liquidity premium for the multinomial model. This paper concentrates on a multinomial tree with unbounded jumps, which can be seen as an extension of the work of (Xing, 2015). The super-replication cost and the liquidity premium under the variance gamma model and the normal inverse Gaussian model are calculated and illustrated.

Image denoising using normal inverse gaussian model in quaternion wavelet domain

2013 ◽  
Vol 74 (3) ◽  
pp. 1107-1124 ◽  
Author(s):  
Shan Gai ◽  
Limin Luo
Keyword(s):  
Image Denoising ◽  
Gaussian Model ◽  
Wavelet Domain ◽  
Inverse Gaussian ◽  
Normal Inverse Gaussian
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