INSIST (International Series on Interdisciplinary Research)
Vol 3, No 2 (2018)

Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion

Setiawan, Eri (Unknown)
Herawati, Netti (Unknown)
Nisa, Khoirin (Unknown)

Article Info

Publish Date
20 Oct 2018


The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

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Journal Info







INSIST is an International online journal which publishes innovative research papers and critical reviews in the field of engineering and interdisciplinary science researches. It focuses on but not limited to Electrical and Telecommunication, Mechanical Engineering, Chemical and Environmental ...