Setiawan, Eri and Herawati, Netti and Nisa, Khoirin MODELING STOCK RETURN DATA USING ASYMMETRIC VOLATILITY MODELS : A PERFORMANCE COMPARISON BASED ON THE AKAIKE INFORMATION CRITERION AND SCHWARZ CRITERION. INSIST. ISSN eISSN: 2502-8588 (Submitted)

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Abstract

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.

Item Type:	Article
Subjects:	H Social Sciences > HA Statistics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika
Depositing User:	Dr NETTI HERAWATI
Date Deposited:	05 Jun 2018 07:24
Last Modified:	05 Jun 2018 07:24
URI:	http://repository.lppm.unila.ac.id/id/eprint/7021

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