Modeling and forecasting volatility of the Malaysian stock markets

Ahmed Shamiri, Zaidi Isa

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

Original languageEnglish
Pages (from-to)234-240
Number of pages7
JournalJournal of Mathematics and Statistics
Volume5
Issue number3
DOIs
Publication statusPublished - 2009

Fingerprint

Volatility Forecasting
Stock Market
Volatility
Forecast
Modeling
Forecasting
Skew-normal Distribution
Inverse Gaussian
Model Selection Criteria
GARCH Model
Model
Natural Selection
Generalized Autoregressive Conditional Heteroscedasticity
Skewness
Finance
Skew
Gaussian distribution
Specification
Moment
Evaluation

Keywords

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

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Modeling and forecasting volatility of the Malaysian stock markets. / Shamiri, Ahmed; Isa, Zaidi.

In: Journal of Mathematics and Statistics, Vol. 5, No. 3, 2009, p. 234-240.

Research output: Contribution to journalArticle

@article{f7167d71eeb942a79708c7615fba4070,
title = "Modeling and forecasting volatility of the Malaysian stock markets",
abstract = "Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.",
keywords = "Asymmetry, GARCH-models, Stock market indices and volatility modeling",
author = "Ahmed Shamiri and Zaidi Isa",
year = "2009",
doi = "10.3844/jmssp.2009.234.240",
language = "English",
volume = "5",
pages = "234--240",
journal = "Journal of Mathematics and Statistics",
issn = "1549-3644",
publisher = "Science Publications",
number = "3",

}

TY - JOUR

T1 - Modeling and forecasting volatility of the Malaysian stock markets

AU - Shamiri, Ahmed

AU - Isa, Zaidi

PY - 2009

Y1 - 2009

N2 - Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

AB - Problem statement: One of the main purposes of modeling variance is forecasting, which is crucial in many areas of finance. Despite the burgeoning interest in and evaluation of volatility forecasts, a clear consensus on witch volatility model/or distribution specification to use has not yet been reached. Therefore, the out of-sample forecasting ability should be a natural model selection criterion for volatility models. Approach: In this study, we used high-frequency to facilitate meaningful comparison of volatility forecast models. We compared the performance of symmetric GARCH, asymmetric EGARCH and non leaner asymmetric NAGARCH models with six error distributions (normal, skew normal, student-t, skew student-t, generalized error distribution and normal inverse Gaussian). Results: The results suggested that allowing for a heavy-tailed error distribution leads to significant improvements in variance forecasts compared to using normal distribution. It was also found that allowing for skewness in the higher moments of the distribution did not further improve forecasts. Conclusion: Successful volatility model forecast depended much more heavily on the choice of error distribution than the choice of GARCH models.

KW - Asymmetry

KW - GARCH-models

KW - Stock market indices and volatility modeling

UR - http://www.scopus.com/inward/record.url?scp=69549091981&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=69549091981&partnerID=8YFLogxK

U2 - 10.3844/jmssp.2009.234.240

DO - 10.3844/jmssp.2009.234.240

M3 - Article

AN - SCOPUS:69549091981

VL - 5

SP - 234

EP - 240

JO - Journal of Mathematics and Statistics

JF - Journal of Mathematics and Statistics

SN - 1549-3644

IS - 3

ER -