Long memory and asymmetric volatility behaviour of the Malaysian stock market

A statistical modelling approach

Abu Hassan Shaari Md Nor, Wen Cheong Chin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student-t and skewed Student-t. The stock returns'long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).

Original languageEnglish
Pages (from-to)67-73
Number of pages7
JournalSains Malaysiana
Volume35
Issue number1
Publication statusPublished - Jul 2006

Fingerprint

Long memory
Asymmetric volatility
Stock market
Modeling
Composite index
GARCH model
Generalized autoregressive conditional heteroscedasticity
Predictive ability
Student-t distribution
Stock returns
Integrated
Innovation
Malaysia

Keywords

  • Fractional integration
  • Leverage effect
  • Long memory
  • Self-similar
  • Volatility

ASJC Scopus subject areas

  • General

Cite this

Long memory and asymmetric volatility behaviour of the Malaysian stock market : A statistical modelling approach. / Md Nor, Abu Hassan Shaari; Chin, Wen Cheong.

In: Sains Malaysiana, Vol. 35, No. 1, 07.2006, p. 67-73.

Research output: Contribution to journalArticle

@article{4550259594bf491692d81b2686e53eda,
title = "Long memory and asymmetric volatility behaviour of the Malaysian stock market: A statistical modelling approach",
abstract = "This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student-t and skewed Student-t. The stock returns'long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).",
keywords = "Fractional integration, Leverage effect, Long memory, Self-similar, Volatility",
author = "{Md Nor}, {Abu Hassan Shaari} and Chin, {Wen Cheong}",
year = "2006",
month = "7",
language = "English",
volume = "35",
pages = "67--73",
journal = "Sains Malaysiana",
issn = "0126-6039",
publisher = "Penerbit Universiti Kebangsaan Malaysia",
number = "1",

}

TY - JOUR

T1 - Long memory and asymmetric volatility behaviour of the Malaysian stock market

T2 - A statistical modelling approach

AU - Md Nor, Abu Hassan Shaari

AU - Chin, Wen Cheong

PY - 2006/7

Y1 - 2006/7

N2 - This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student-t and skewed Student-t. The stock returns'long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).

AB - This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student-t and skewed Student-t. The stock returns'long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).

KW - Fractional integration

KW - Leverage effect

KW - Long memory

KW - Self-similar

KW - Volatility

UR - http://www.scopus.com/inward/record.url?scp=34548736454&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548736454&partnerID=8YFLogxK

M3 - Article

VL - 35

SP - 67

EP - 73

JO - Sains Malaysiana

JF - Sains Malaysiana

SN - 0126-6039

IS - 1

ER -