The inverse Gaussian process with a skew-normal distribution as a degradation model

2019 ◽  
Vol 49 (11) ◽  
pp. 2827-2843 ◽  
Author(s):  
Xudan Chen ◽  
Xinli Sun ◽  
Xiong Ding ◽  
Jue Tang
Keyword(s):  
Gaussian Process ◽  
Normal Distribution ◽  
Inverse Gaussian ◽  
Skew Normal Distribution ◽  
Degradation Model ◽  
Inverse Gaussian Process ◽  
Skew Normal

scholarly journals Increments of Normal Inverse Gaussian Process as Logarithmic Returns of Stock Price

2018 ◽  
Vol 21 ◽  
pp. 93-97
Author(s):  
Oskars Rubenis ◽  
Andrejs Matvejevs
Keyword(s):  
Gaussian Process ◽  
Normal Distribution ◽  
Stock Prices ◽  
Stock Price ◽  
High Accuracy ◽  
Price Dynamics ◽  
Inverse Gaussian ◽  
Inverse Gaussian Process ◽  
Normal Inverse Gaussian ◽  
New Distribution

Normal inverse Gaussian (NIG) distribution is quite a new distribution introduced in 1997. This is distribution, which describes evolution of NIG process. It appears that in many cases NIG distribution describes log-returns of stock prices with a high accuracy. Unlike normal distribution, it has higher kurtosis, which is necessary to fit many historical returns. This gives the opportunity to construct precise algorithms for hedging risks of options. The aim of the present research is to evaluate how well NIG distribution can reproduce stock price dynamics and to illuminate future fields of application.

scholarly journals Some properties of the unified skew-normal distribution

2021 ◽  
Author(s):  
Reinaldo B. Arellano-Valle ◽  
Adelchi Azzalini
Keyword(s):  
Normal Distribution ◽  
Fourth Order ◽  
Probability Distributions ◽  
Skew Normal Distribution ◽  
Present Contribution ◽  
The Family ◽  
Multivariate Skewness ◽  
Unified Skew Normal Distribution ◽  
Skewness And Kurtosis ◽  
Skew Normal

AbstractFor the family of multivariate probability distributions variously denoted as unified skew-normal, closed skew-normal and other names, a number of properties are already known, but many others are not, even some basic ones. The present contribution aims at filling some of the missing gaps. Specifically, the moments up to the fourth order are obtained, and from here the expressions of the Mardia’s measures of multivariate skewness and kurtosis. Other results concern the property of log-concavity of the distribution, closure with respect to conditioning on intervals, and a possible alternative parameterization.

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