MODELLING ABRUPT SHIFT IN TIME SERIES USING INDICATOR VARIABLE: EVIDENCE FROM NIGERIAN INSURANCE STOCK
MODELLING ABRUPT SHIFT IN TIME SERIES USING INDICATOR VARIABLE: EVIDENCE FROM NIGERIAN INSURANCE STOCK
No Thumbnail Available
Date
2015-07
Authors
AGBOOLA, Samson
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Abstract
This study models abrupt shift in time series using indicator variable: evidence from Nigeria
Insurance stocks. Data on daily closing prices for some selected Nigerian Insurance stocks were
collected between 2nd
January, 2000 and 26th May, 2014. Daily returns were then computed
from thedaily prices. To study the volatility pattern of Insurance stock, seven symmetric models
and five asymmetric models with dummy variables incorporated to their variance equation were
considered and these are ARCH (1), ARCH (2), ARCH (3), GARCH (1, 1), GARCH (2, 1),
GARCH (1, 2), GARCH (2, 2), EGARCH (1, 1), EGARCH (1, 2), EGARCH (2, 1), EGARCH
(2, 2) and TARCH (1, 1). Post estimation and performance evolution metric was evaluated using
the RMSE, MAE and MAPE. The results showed that, the daily returns were stationary but not
normally distributed and eight out of ten stocks considered for the study showed evidence of
ARCH effect. Furthermore, the results of the post estimation revealed that most of the models
were competitive and model ARCH (1) and EGARCH (1, 1) proved to be the most suitable
among the twelve competing volatility models considered in some of the Insurance company.
This present study findings are very crucial and informative to investors and intending investors
as it will help in stock pricing strategy as volatility is the major index used to evaluate asset
performance and stock pricing strategy.
Description
A THESIS SUBMITTED TO THE POSTGRADUATE SCHOOL,
AHMADU BELLO UNIVERSITY, ZARIA
NIGERIA
IN PARTIAL FULFILLMENT FOR THE AWARD OF MASTER OF
SCIENCE (M.Sc) DEGREE IN STATISTICS
DEPARTMENT OF MATHEMATICS
AHMADU BELLO UNIVERSITY, ZARIA
NIGERIA
Keywords
MODELLING, ABRUPT, SHIFT, INDICATOR, VARIABLE, EVIDENCE, INSURANCE, STOCK