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

Abstract Number:

3194 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Hasan Hamdan (1), Mozhdeh Forghaniarani (2)

Institutions:

(1) James Madison University, N/A, (2) N/A, N/A

Co-Author:

Mozhdeh Forghaniarani  
N/A

First Author:

Hasan Hamdan  
James Madison University

Presenting Author:

Mozhdeh Forghaniarani  
N/A

Abstract Text:

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| |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Applied Sciences

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