Using and Evaluating Infinite Multivariate Scale Mixtures of Normals to Estimate the CCR Density

Mozhdeh Forghaniarani Co-Author
 
Hasan Hamdan First Author
James Madison University
 
Mozhdeh Forghaniarani Presenting Author
 
Thursday, Aug 8: 9:50 AM - 10:05 AM
3194 
Contributed Papers 
Oregon Convention Center 
In this study, we will use infinite multivariate scale mixtures of Normal distributions to model the density of the continuously compounded return, CCR, of five major American stocks. The approach is based on using a finite discretized version of the density, then estimating the parameters of the corresponding multivariate density based on that of the univariate components using UNMIX, which is a newly developed program for estimating and fitting univariate infinite scale mixtures of Normals. The fitted density is compared with the empirical and the Bayesian method of estimation to the same data. The CCR of the weekly closing stock prices of Google, Amazon, Exxon Mobile, Apple and Fannie Mae and maybe few others. We also check the effect of correlation on the fits.

Keywords

Multivariate Scale Mixtures

Bayesian Method

UNMIX

CCR 

Main Sponsor

Section on Bayesian Statistical Science