Using Scale Mixtures Of Normals To Model Continuously Compounded Returns
Article Sidebar
Published
May 1, 2005
Main Article Content
Hasan Hamdan
Department of Mathematics and Statistics, James Madison University
John Nolan
Department of Mathematics and Statistics, American University
Melanie Wilson
Department of Mathematics, Allegheny College
Kristen Dardia
Department of Mathematics and Statistics, James Madison University
Abstract
A new method for estimating the parameters of scale mixtures of normals (SMN) is introduced and evaluated. The new method is called UNMIX and is based on minimizing the weighted square distance between exact values of the density of the scale mixture and estimated values using kernel smoothing techniques over a specified grid of x-values and a grid of potential scale values. Applications of the method are made in modeling the continuously compounded return, CCR, of stock prices. Modeling this ratio with UNMIX proves promising in comparison with other existing techniques that use only one normal component, or those that use more than one component based on the EM algorithm as the method of estimation.
Article Details
Issue
Section
Articles