An evaluation of univariate time-series models of quarterly earnings per share and their generalization to models with autoregressive conditionally heteroscedastic disturbances

This study evaluates time-series models of quarterly earnings per share (EPS) in order to determine whether there are any changes in the residual variance to be modeled by the GARCH procedure. The results of statistical analyses indicate the presence of GARCH effect in the residuals generated from ARIMA models for quarterly EPS. Furthermore, based on Akaike's information criterion, modeling the GARCH effect appears to be desirable. However, the results of forecast accuracy comparisons provide no evidence that the ARIMA-GARCH specification results in more accurate forecasts than the conventional ARIMA models.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Accountancy

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Roy, Daniel

Contributors dc:contributor

• McKeown, James C.

• Business Administration, Accounting

Statement dc:rights

• Copyright 1992 Roy, Daniel

Language dc:language

eng

Identifier

AAI9236585
(UMI)AAI9236585

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/23358