scholarly journals Multivariate Distribution in the Stock Markets of Brazil, Russia, India, and China

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110095
Author(s):  
Leovardo Mata Mata ◽  
José Antonio Núñez Mora ◽  
Ramona Serrano Bautista
Keyword(s):  
Financial Markets ◽  
Stock Markets ◽  
Value At Risk ◽  
Robust Estimator ◽  
Inverse Gaussian ◽  
Multivariate Normal ◽  
Estimated Parameters ◽  
Gaussian Probability Distribution ◽  
Normal Inverse Gaussian ◽  
India And China

The purpose of this article is to analyze the dependence between Brazil, Russia, India, and China (BRIC) stock markets, adjusting the multivariate Normal Inverse Gaussian probability distribution (NIG) in 2010–2019 on data yields. Using the estimated parameters, a robust estimator of the correlation matrix is calculated, and evidence is found of the degree of integration in BRIC financial markets during the period 2000–2019. In addition, it is found that the Value at Risk presents a better performance when using the NIG distribution versus multivariate generalized autoregressive conditional heteroscedastic models.

scholarly journals A QUASI-MONTE CARLO ALGORITHM FOR THE NORMAL INVERSE GAUSSIAN DISTRIBUTION AND VALUATION OF FINANCIAL DERIVATIVES

2006 ◽  
Vol 09 (06) ◽  
pp. 843-867 ◽  
Author(s):  
FRED ESPEN BENTH ◽  
MARTIN GROTH ◽  
PAUL C. KETTLER
Keyword(s):  
Monte Carlo ◽  
Value At Risk ◽  
Scale Parameter ◽  
Monte Carlo Algorithm ◽  
Asian Options ◽  
Inverse Gaussian ◽  
Option Prices ◽  
Quasi Monte Carlo ◽  
Normal Inverse Gaussian Distribution ◽  
Normal Inverse Gaussian

We propose a quasi-Monte Carlo (qMC) algorithm to simulate variates from the normal inverse Gaussian (NIG) distribution. The algorithm is based on a Monte Carlo technique found in Rydberg [13], and is based on sampling three independent uniform variables. We apply the algorithm to three problems appearing in finance. First, we consider the valuation of plain vanilla call options and Asian options. The next application considers the problem of deriving implied parameters for the underlying asset dynamics based on observed option prices. We employ our proposed algorithm together with the Newton Method, and show how we can find the scale parameter of the NIG-distribution of the logreturns in case of a call or an Asian option. We also provide an extensive error analysis for this method. Finally we study the calculation of Value-at-Risk for a portfolio of nonlinear products where the returns are modeled by NIG random variables.

scholarly journals Clustering with the multivariate normal inverse Gaussian distribution

2016 ◽  
Vol 93 ◽  
pp. 18-30 ◽  
Author(s):  
Adrian O’Hagan ◽  
Thomas Brendan Murphy ◽  
Isobel Claire Gormley ◽  
Paul D. McNicholas ◽  
Dimitris Karlis
Keyword(s):  
Gaussian Distribution ◽  
Inverse Gaussian Distribution ◽  
Inverse Gaussian ◽  
Multivariate Normal ◽  
Normal Inverse Gaussian Distribution ◽  
Normal Inverse Gaussian

Multivariate Shrinkage for Image Denoising in Shearlet Domain

2012 ◽  
Vol 239-240 ◽  
pp. 966-969
Author(s):  
Cheng Zhi Deng
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Threshold Function ◽  
Maximum A Posteriori ◽  
Inverse Gaussian ◽  
A Posteriori ◽  
Multivariate Normal ◽  
Gaussian Probability Density Function ◽  
Normal Inverse Gaussian

A new multivariate threshold function for image denoising in the shearlet transfrom is proposed. The new threshod exploits a multivariate normal inverse gaussian probability density function to model neighboring shearlet coefficients. Under this prior, a multivariate Bayesian shearlet estimator is derived by using the maximum a posteriori rule. Experimental results show that the new method achieves state-of-art performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index and visual quality than existing shearlet-based image denoising methods.

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