Forecasting Volatility in the New Zealand Stock Market
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 mod...
Saved in:
Main Author: | |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2002
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/soe_research/413 https://ink.library.smu.edu.sg/context/soe_research/article/1412/viewcontent/YuAFE.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.soe_research-1412 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.soe_research-14122018-07-13T05:23:13Z Forecasting Volatility in the New Zealand Stock Market YU, Jun 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] 2002-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/413 info:doi/10.1080/09603100110090118 https://ink.library.smu.edu.sg/context/soe_research/article/1412/viewcontent/YuAFE.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Econometrics Finance |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Econometrics Finance |
spellingShingle |
Econometrics Finance YU, Jun Forecasting Volatility in the New Zealand Stock Market |
description |
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] |
format |
text |
author |
YU, Jun |
author_facet |
YU, Jun |
author_sort |
YU, Jun |
title |
Forecasting Volatility in the New Zealand Stock Market |
title_short |
Forecasting Volatility in the New Zealand Stock Market |
title_full |
Forecasting Volatility in the New Zealand Stock Market |
title_fullStr |
Forecasting Volatility in the New Zealand Stock Market |
title_full_unstemmed |
Forecasting Volatility in the New Zealand Stock Market |
title_sort |
forecasting volatility in the new zealand stock market |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2002 |
url |
https://ink.library.smu.edu.sg/soe_research/413 https://ink.library.smu.edu.sg/context/soe_research/article/1412/viewcontent/YuAFE.pdf |
_version_ |
1770569155572924416 |