Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets

Phichhang Ou, Hengshan Wang

Abstract


In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities of three major ASEAN stock markets. More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.


Full Text: PDF DOI: 10.5539/ijef.v2n1p51

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This work is licensed under a Creative Commons Attribution 3.0 License.

International Journal of Economics and Finance  ISSN  1916-971X (Print) ISSN  1916-9728 (Online)

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