Cover Image

An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong

The relationship between trading volume and return volatility has long been debated either on the contemporaneous correlation as explained by the mixture distribution hypothesis (MDH) or causal (lead-lag) relation as suggested by the sequential information arrival hypothesis (SIAH).The former is pro...

Full description

Saved in:
Bibliographic Details
Main Authors: Tan, Yan Ling, Toy, Bee Hoong
Format: Article
Language:English
Published: 2011
Subjects:
Analysis
Online Access:https://ir.uitm.edu.my/id/eprint/5887/1/5887.pdf
https://ir.uitm.edu.my/id/eprint/5887/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uitm.ir.5887
record_format eprints
spelling my.uitm.ir.58872022-05-14T16:11:24Z https://ir.uitm.edu.my/id/eprint/5887/ An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong Tan, Yan Ling Toy, Bee Hoong Analysis The relationship between trading volume and return volatility has long been debated either on the contemporaneous correlation as explained by the mixture distribution hypothesis (MDH) or causal (lead-lag) relation as suggested by the sequential information arrival hypothesis (SIAH).The former is proposed by Clark (1973), and the latter by Copeland (1976) and Jennings, Starks, and Fellingham (1981).The purpose of this study is empirically to test the relationship between trading volume and return volatility from 3 January 2000 to 31 July 2008 in Malaysia. In this study, GARCH model is chosen because it gives better estimates in modeling return volatility. The contemporaneous correlation is tested by employing simultaneous approach (GARCH-cum trading volume). Our results strongly support the MDH hypothesis since both variables are found to follow a contemporaneous correlation pattern in Malaysia stocks. Moreover including trading volume in the conditional variance (return volatility) equation leads in a reduction of volatility persistence. We also suggest that trading volume is a good proxy of information arrival in the GARCH model. Therefore, the changes in trading volume can be used when formulating new strategy, instead of taking into account of changes in price. 2011 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/5887/1/5887.pdf (2011) An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong. Academic Journal UiTM Johor.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Analysis
spellingShingle Analysis
Tan, Yan Ling
Toy, Bee Hoong
An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
description The relationship between trading volume and return volatility has long been debated either on the contemporaneous correlation as explained by the mixture distribution hypothesis (MDH) or causal (lead-lag) relation as suggested by the sequential information arrival hypothesis (SIAH).The former is proposed by Clark (1973), and the latter by Copeland (1976) and Jennings, Starks, and Fellingham (1981).The purpose of this study is empirically to test the relationship between trading volume and return volatility from 3 January 2000 to 31 July 2008 in Malaysia. In this study, GARCH model is chosen because it gives better estimates in modeling return volatility. The contemporaneous correlation is tested by employing simultaneous approach (GARCH-cum trading volume). Our results strongly support the MDH hypothesis since both variables are found to follow a contemporaneous correlation pattern in Malaysia stocks. Moreover including trading volume in the conditional variance (return volatility) equation leads in a reduction of volatility persistence. We also suggest that trading volume is a good proxy of information arrival in the GARCH model. Therefore, the changes in trading volume can be used when formulating new strategy, instead of taking into account of changes in price.
format Article
author Tan, Yan Ling
Toy, Bee Hoong
author_facet Tan, Yan Ling
Toy, Bee Hoong
author_sort Tan, Yan Ling
title An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
title_short An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
title_full An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
title_fullStr An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
title_full_unstemmed An empirical analysis of trading volume and return volatility in using GARCH model: the Malaysia case / Tan Yan Ling and Toy Bee Hoong
title_sort empirical analysis of trading volume and return volatility in using garch model: the malaysia case / tan yan ling and toy bee hoong
publishDate 2011
url https://ir.uitm.edu.my/id/eprint/5887/1/5887.pdf
https://ir.uitm.edu.my/id/eprint/5887/
_version_ 1732948327607042048
score 13.211869

Similar Items