Volatility Forecasting Using GARCH Versus EGARCH Models for Cryptocurrencies, Indonesian Stocks, and U.S. Stocks
(1) Pelita Bangsa University
(2) Pelita Bangsa University
(3) Pelita Bangsa University
(*) Corresponding Author
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A.K., U.C., & O. (2023). Garch Models Comparison with Symmetric and Asymmetric Process for Univariate Econometric Series. African Journal of Mathematics and Statistics Studies, 6(2), 1–23. https://doi.org/10.52589/ajmss-jdz6zoxg
Almansour, Alshater, & Almansour. (2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering and Management Systems, 20(2), 130–139. https://doi.org/10.7232/iems.2021.20.2.130
Almonifi. (2023). STUDYING VOLATILITY IN SAUDI STOCK MARKET USING ARCH AND GARCH MODELS: A CASE STUDY OF AL RAJHI BANK. International Journal of Islamic Banking and Finance Research, 9–19. https://doi.org/10.46281/ijibfr.v11i1.1986
Arashi, & Rounaghi. (2022). Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model. Future Business Journal, 8(1), 1–12. https://doi.org/10.1186/s43093-022-00125-9
Babalos, Caporale, & Spagnolo. (2021). Equity fund flows and stock market returns in the USA before and after the global financial crisis:Babalos, V., Caporale, G. M., & Spagnolo, N. (2021). Equity fund flows and stock market returns in the USA before and after the global financial crisis: a V. Empirical Economics, 60(2), 539–555. https://doi.org/10.1007/s00181-019-01783-5
Bursa Efek Indonesia. (2024). 50 Biggest Market Capitalization - Oktober 2024. https://idx.co.id/id/data-pasar/laporan-statistik/digital-statistic/monthly/biggest-market-capitalization-most-active-stocks/biggest-market-capitalization?filter=eyJ5ZWFyIjoiMjAyMyIsIm1vbnRoIjoiMyIsInF1YXJ0ZXIiOjAsInR5cGUiOiJtb250aGx5In0%3D
Chi, & Hao. (2021). Volatility models for cryptocurrencies and applications in the options market. Journal of International Financial Markets, Institutions and Money, 75(September 2020), 101421. https://doi.org/10.1016/j.intfin.2021.101421
Ghysels, & Santa-clara. (2004). Predicting Volatility : Getting the Most out of Return Data Sampled at Different Frequencies ∗ Rossen Valkanov. Economic and Finance, 3(919), 1–44.
Guirguis. (2024). Application of a TGARCH, EGARCH, and PARCH models to test the volatility clusters of Shiba, Bitcoin and Ethereum digital cryptocurrencies. Biographical notes. https://ssrn.com/abstract=4768004
Handayani, Farlian, & Ardian. (2019). Firm Size, Market Risk, and Stock Return: Evidence from Indonesian Blue Chip Companies. Jurnal Dinamika Akuntansi Dan Bisnis, 6(2), 171–182. https://doi.org/10.24815/jdab.v6i2.13082
Ikrima, & Surya Darmawan. (2023). Analisis Volatily Spillover Bitcoin Terhadap Ethereum, Tether, dan Emas Dunia Menggunakan Metode EGARCH. Jurnal Manajemen Dan Perbankan (JUMPA), 10(2), 47–60. https://doi.org/10.55963/jumpa.v10i2.555
Jin, Li, & Li. (2022). Modeling the Linkages between Bitcoin, Gold, Dollar, Crude Oil, and Stock Markets: A GARCH-EVT-Copula Approach. Discrete Dynamics in Nature and Society, 2022. https://doi.org/10.1155/2022/8901180
Kurnaman, & Rizal. (2023). The Relationship Between Bitcoin Returns, Volatility, And Volume Using Asymmetric Garch Modelling. JAF- Journal of Accounting and Finance, 7(1), 1. https://doi.org/10.25124/jaf.v7i1.5565
Mamilla, Kathiravan, Salamzadeh, Dana, & Elheddad. (2023). COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models. Journal of Risk and Financial Management, 16(10). https://doi.org/10.3390/jrfm16100447
Martinet, & McAleer. (2018). On the invertibility of EGARCH(p, q). Econometric Reviews, 37(8), 824–849. https://doi.org/10.1080/07474938.2016.1167994
Meher, Kumar, Birau, Kumar, Ana, Lupu, Simion, & Cirjan. (2024). Comparative Volatility Analysis of USA and China Stock Market Indices using GARCH Family Models Abstract : 83, 108–128.
Naik, Mohan, & Jha. (2020). GARCH-Model Identification based on Performance of Information Criteria. Procedia Computer Science, 171, 1935–1942. https://doi.org/10.1016/j.procs.2020.04.207
Nakamoto. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. -. https://doi.org/10.1108/TG-06-2020-0114
Ngunyi, Mundia, & Omari. (2019). Modelling Volatility Dynamics of Cryptocurrencies Using GARCH Models. Journal of Mathematical Finance, 09(04), 591–615. https://doi.org/10.4236/jmf.2019.94030
Nur Fadhilah, Parmikanti, & Nurani Ruchjana. (2024). Peramalan Return Saham Subsektor Perbankan Menggunakan Model ARIMA-GARCH. 13(1), 1–19. https://doi.org/10.14421/fourier.2024.131.1-19
Nurhasanah. (2018). Comparison of Modeling Volatility of Indonesia Banks Using ARCH, GARCH, TARCH and EGARCH. Jurnal Manajemen Dan Bisnis: Performa, 15(2).
PujiAstuti, & Suwanda. (2022). Evaluasi Model Exponential Generelized Autoregressive Conditional Heteroscedastic (EGARCH). Bandung Conference Series: Statistics, 2(2), 358–364. https://doi.org/10.29313/bcss.v2i2.4365
Rehman, Ahmad, Desheng, & Karamoozian. (2024). Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches. http://arxiv.org/abs/2407.15766
Rio Rita, Wahyudi, & Muharam. (2018). Bad Friday, Monday Effect and Political Issue: Application of ARCH-GARCH Model to Analyze Seasonal Pattern of Stock Return. International Journal of Engineering & Technology, 7(3.30), 38. https://doi.org/10.14419/ijet.v7i3.30.18152
Rizki, Ammar, Fitriyani, Fasya, Irfan, Ammar, Departemen Statistika, Barat, Rizki, Ammar, Fitriyani, & Fasya. (2021). Peramalan Indeks Harga Saham PT Verena Multi Finance Tbk Dengan Metode Pemodelan ARIMA Dan ARCH-GARCH. 14(1), 11–23. https://doi.org/10.36456/jstat.vol14.no1.a3774
Tiwari, Raheem, & Kang. (2019). Time-varying dynamic conditional correlation between stock and cryptocurrency markets using the copula-ADCC-EGARCH model. Physica A: Statistical Mechanics and Its Applications, 535, 122295. https://doi.org/10.1016/j.physa.2019.122295
Trifanni, Permana, Amalita, & Putra. (2023). Time Series ARIMA and Asymmetric GARCH Modeling on Stock Return at PT. Telecommunication Indonesia Tbk. In UNP JOURNAL OF STATISTICS AND DATA SCIENCE (Vol. 1).
Virginia, Ginting, & Elfaki. (2018). Application of garch model to forecast data and volatility of share price of energy (Study on adaro energy Tbk, LQ45). International Journal of Energy Economics and Policy, 8(3), 131–140.
Widyanti, Sudarno, & Widiharih. (2023). ANALISIS VOLATILITAS BITCOIN MENGGUNAKAN MODEL ARCH DAN GARCH. Jurnal Gaussian, 12(2), 254–265. https://doi.org/10.14710/j.gauss.12.2.254-265
Yıldırım, & Bekun. (2023). Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models. Future Business Journal, 9(1). https://doi.org/10.1186/s43093-023-00255-8
Yussif, Onifade, Ay, Canitez, & Bekun. (2024). Modeling the volatility of exchange rate and international trade in Ghana: empirical evidence from GARCH and EGARCH. Journal of Economic and Administrative Sciences, 40(2), 308–324. https://doi.org/10.1108/jeas-11-2020-0187
Zhang. (2024). Research on AAPL Stock Price Prediction Using ARIMA Model. Advances in Economics, Management and Political Sciences, 88(1), 226–234. https://doi.org/10.54254/2754-1169/88/20240914
DOI: http://dx.doi.org/10.33019/ijbe.v9i2.1125
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