Evaluating the Forecasting Performance of Symmetric and Asymmetric GARCH Models across Stock Markets

N.Chitra Devi

Evaluating the Forecasting Performance of Symmetric and Asymmetric GARCH Models across Stock Markets

N.Chitra Devi * §

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§ Doms, NIT, Trichy

Evaluating the Forecasting Performance of Symmetric and Asymmetric GARCH Models across Stock Markets

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Abstract

Recently, the stock market volatility has created a surge among the researchers to focus their attention towards studying the sensitivity of stock market returns. In this study, the method of OLS has been applied to study the sensitivity of stock market returns to macroeconomic fundamentals. The performance of OLS (Ordinary Least Square Method) has not been BLUE (Best Linear Unbiased Estimator) due to the existence of heteroskedasticity. The presence of heteroskedasticity is confirmed by the ARCH LM test of Heteroskedasticity. Therefore, Symmetric and Asymmetric GARCH models have been employed to investigate the interaction between the stock market volatility and macroeconomic fundamentals volatility. Apart from this, the forecasting performance of symmetric and asymmetric GARCH models are compared and ranked based on the error measurement approaches such as Mean Squared Error, Root mean squared error and Mean Absolute Percentage Error. The results of the Mean Absolute Percentage Error reveals that the asymmetric E-GARCH model is the superior model to other GARCH models namely TGARCH and symmetric GARCH models in explaining the stock market returns in USA and in UK. Subsequently, the GARCH models outperform well in the US stock market comparing with the UK stock market.

References

22 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

N.Chitra Devi. 2018. "Evaluating the Forecasting Performance of Symmetric and Asymmetric GARCH Models across Stock Markets". Global Journal of Management and Business Research, Global Journal of Management and Business Research - B: Economic & Commerce GJMBR-B Volume 18 (GJMBR Volume 18 Issue B2).

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GJMBR Volume 18 Issue B2

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Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

Keywords

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Classification

GJMBR-B Classification JEL Code: A19

Submission ReceivedDecember 11, 2017
Peer Review Double Blind
Handling Editor
Accepted January 3, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

May 8, 2018

Language

English

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Total Score: 148

Country: India

Subject: Global Journal of Management and Business Research

Authors: N.Chitra Devi (PhD/Dr. count: 0)

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