Publication Type
Journal Article
Version
publishedVersion
Publication Date
2002
Abstract
This study evaluates the performance of nine alternative models for predicting stock price volatility using daily New Zealand data. The competing models contain both simple models such as the random walk and smoothing models and complex models such as ARCH-type models and a stochastic volatility model. Four different measures are used to evaluate the forecasting accuracy. The main results are the following: (1) the stochastic volatility model provides the best performance among all the candidates; (2) ARCH-type models can perform well or badly depending on the form chosen: the performance of the GARCH(3,2) model, the best model within the ARCH family, is sensitive to the choice of assessment measures; and (3) the regression and exponentially weighted moving average models do not perform well according to any assessment measure, in contrast to the results found in various markets. [ABSTRACT FROM AUTHOR]
Discipline
Econometrics | Finance
Research Areas
Econometrics
Publication
Applied Financial Economics
Volume
12
Issue
3
First Page
193
Last Page
202
ISSN
0960-3107
Identifier
10.1080/09603100110090118
Publisher
Taylor and Francis
Citation
YU, Jun.
Forecasting Volatility in the New Zealand Stock Market. (2002). Applied Financial Economics. 12, (3), 193-202.
Available at: https://ink.library.smu.edu.sg/soe_research/413
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/09603100110090118