Publication Date

12-1-2017

Document Type

Dissertation/Thesis

First Advisor

Anderson, Evan W.

Degree Name

B.S. (Bachelor of Science)

Legacy Department

Department of Economics

Abstract

This paper examines efficacy and limitations of time series models, namely ARIMA, GARCH, and ARMA-GARCH for stock market returns forecasting. First, the paper assesses the unique features of financial data, particularly volatility clustering and fat-tails of the return distribution, and addresses the limitations of using autoregressive integrated moving average (ARIMA) models in financial economics. Secondly, it examines the application of ARMA-GARCH models for forecasting of both conditional means as well as the conditional variance of the returns. Finally, using the standard model selection criteria such as AIC, BIC, SIC, and HQIC the forecasting performance of various candidate ARMA-GARCH models was examined. Using excess returns of MSCI World Index and excess returns from Fama-French 3-factor-model, it was found that an ARMA (1,0) + GARCH (1,1) consistently yields best results in-sample for the same period across both datasets, while showing some forecasting limitations out-of-sample.

Extent

51 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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