Modelling of Market Stock Using the Normal Variance Models
Abstract
Great work has been done on modeling of nancial instruments namely,shares,equities,
stocks and many more.The focus of this thesis is mainly modeling of stocks based on
normal mixtures.The essence of this work is to do a comparison between the Normal
Variance Mean Model and Normal Variance model and determine which of the two is best
for modeling stocks.
Normal mixtures is a combination of two distributions where the normal distribution is
the conditional distribution and is mixed with another distribution as the mixing distribution.
The two mixing distribution discussed in this thesis are both Gamma and Inverse
Gaussian distributions ,out of which we get the Variance Gamma and Normal Inverse
Gaussian distributions respectively.Data is tted on the distributions, Normal variance
model and the Normal Variance Mean Model and a comparison is done to ascertain which
model gives the best goodness of t and is the best model.
Construction of the two distributions based on Normal Variance is done using two approaches
that is; Saralees Nadarajah and Ole Barndo , and their respective properties are
given. Their respective Maximum likelihood Estimators based on the two distributions
is also determined. Estimation of the two models is e ected using Method of Moments.
Data from Standard and Poor’s 500 index,January 1977- December has been used for the
analysis of the given work,and the results have been discussed accordingly. With regard to
AIC test done in the analysis Skewed Variance Gamma distribution gives the best goodness
of t as compared to the other distributions While Nadarajah’s Approach Normal Inverse
Gaussian distribution tends to be the model for tting stock returns
Publisher
University of Nairobi
Subject
Modelling of Market StockRights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
The following license files are associated with this item: