Modeling and Forecasting Return Volatilities of Inter-Capital Market Indices using GARCH-Fractional Cointegration Model Variation

This research compares modeling and forecasting the volatility of the IHSG, N225, and BSESN30 capital market indices using the GARCH variation model against the GARCH-fractional cointegration variation. The data used is secondary data obtained from www.investing.com from 01/01/2012 to 04/30/2023. Ba...

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Bibliographic Details
Published in:Procedia computer science 2024, Vol.234, p.389-396
Main Authors: Effendi, Magdalena, Prastyo, Dedy Dwi, Akbar, Muhammad Sjahid
Format: Article
Language:English
Subjects:
Capital Market
Fractional Cointegration
GARCH
Return
Volatility
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Summary:This research compares modeling and forecasting the volatility of the IHSG, N225, and BSESN30 capital market indices using the GARCH variation model against the GARCH-fractional cointegration variation. The data used is secondary data obtained from www.investing.com from 01/01/2012 to 04/30/2023. Based on the performance measurement using the sMAPE criterion, the best model for forecasting the period 05/01/2023 to 05/31/2023 is the std-ALLGARCH (1,2)-fractional cointegration model for IHSG, the std-ALLGARCH(1,1) model for N225, and the sstd-ALLGARCH (1,2) model for BSESN30. This empirical finding means that the Japanese and Indian capital markets affect the volatility of the Indonesian capital market.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.03.019