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p-Index From 2020 - 2025
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This Author published in this journals
All Journal IJBE (Integrated Journal of Business and Economics)
Utami, Asri
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Humanities Social Sciences

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Volatility Forecasting Using GARCH Versus EGARCH Models for Cryptocurrencies, Indonesian Stocks, and U.S. Stocks Dharma, Yuki Dwi; Utami, Asri; Pujiharta, Pujiharta
Integrated Journal of Business and Economics (IJBE) Vol 9, No 2 (2025): Integrated Journal of Business and Economics
Publisher : Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/ijbe.v9i2.1125

Abstract

This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon). Using comparative analysis of ARCH(1), GARCH(1,1), and EGARCH(1,1,1) models, the study evaluates their predictive accuracy through multiple metrics including AIC, MAE, RMSE, and SMAPE. Results indicate that EGARCH(1,1,1) generally performs better for cryptocurrencies and U.S. stocks, while GARCH(1,1) shows superior performance for Indonesian stocks, suggesting that volatility patterns and optimal forecasting models vary across different market contexts.
Co-Authors Dharma, Yuki Dwi Pujiharta, Pujiharta