E, Setiawan and Netti, Herawati and khoirun, Nisa (2020) Modeling Stock Return Data using AsymmetricVolatility Models: A Performance Comparison based on the Akaike Information Criterion andSchwarz Criterion. Journal of Engineering and Scientific Research (JESR), 9 (9). ISSN 2319-8753
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Abstract
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has beenwidely used in time seriesforecastingespecially with asymmetric volatilitydata. As the generalization of autoregressive conditional heteroskedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH modelswere 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 modelsby 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 stockreturn data is the APARCHmodel.Keywords: Volatility, GARCH,TGARCH, EGARCH, APARCH, AIC and SC
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika |
| Depositing User: | Dr NETTI HERAWATI |
| Date Deposited: | 06 May 2021 06:47 |
| Last Modified: | 06 May 2021 06:47 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/30023 |
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