Modeling and forecasting volatility of the Malaysian and the Singporean stock indices using asymmetric GARCH models and non-normal densities

Abu Hassan Shaari Md Nor, A. Shamiri

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper examines and estimates the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using daily price data. Two Asian stock indices KLCI and STI were studied using daily data over a 14-years period. The competing models include GARCH, EGARCH and GJR-GARCH using the Gaussian normal, Student-t and Generalized Error Distributions. The estimates showed that the forecasting performance of asymmetric GARCH Models (GJR-GARCH and EGARCH), especially when fat-tailed densities are taken into account in the conditional volatility, are better than symmetric GARCH. Moreover, it was found that the AR(1)-GJR model provides the best out-of-sample forecast for the Malaysian stock market, while AR(1)-EGARCH provides a better estimation for the Singaporean stock market.

Original languageEnglish
Pages (from-to)83-102
Number of pages20
JournalMalaysian Journal of Mathematical Sciences
Volume1
Issue number1
Publication statusPublished - 2007

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Volatility Forecasting
Stock Index
GARCH Model
Generalized Autoregressive Conditional Heteroscedasticity
Modeling
Stock Market
Volatility
Estimate
Forecast
Forecasting

Keywords

  • ARCH-models
  • Asymmetry
  • Stock market indices and volatility modeling

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Modeling and forecasting volatility of the Malaysian and the Singporean stock indices using asymmetric GARCH models and non-normal densities. / Md Nor, Abu Hassan Shaari; Shamiri, A.

In: Malaysian Journal of Mathematical Sciences, Vol. 1, No. 1, 2007, p. 83-102.

Research output: Contribution to journalArticle

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