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E Setiawan
Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Lampung, Bandar Lampung, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Modeling Stock Return Data using Asymmetric Volatility Models: A Performance Comparison based on the Akaike Information Criterion and Schwarz Criterion E Setiawan; Netti Herawati; K Nisa
Journal of Engineering and Scientific Research Vol. 1 No. 1 (2019)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (918.035 KB) | DOI: 10.23960/jesr.v1i1.8

Abstract

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroskedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among themare 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
Co-Authors K Nisa Netti Herawati