Open AccessModeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion
Author(s) -
Eri Setiawan,
Netti Herawati,
Khoirin Nisa
Publication year - 2018
Publication title -
insist (international series on integrated science and technology)
Language(s) - English
Resource type - Journals
ISSN - 2502-8588
DOI - 10.23960/ins.v3i2.160
Subject(s) - autoregressive conditional heteroskedasticity , akaike information criterion , econometrics , heteroscedasticity , volatility (finance) , autoregressive model , mathematics , economics , statistics
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.