Publication Type

Journal Article

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

Additional URL

http://dx.doi.org/10.1080/09603100110090118

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