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@article{208222,
        author = {Ede Naga Anilkumar and Tirupathi Rao Padi and Godide Meghana and Sarode Shirisha},
        title = {Modelling and Forecasting the Volatility of Bitcoin and Ethereum Using GARCH Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {892-899},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208222},
        abstract = {Cryptocurrencies such as Bitcoin (BTC) and Ethereum (ETH) exhibit pronounced volatility driven by market sentiment, regulatory developments, and macroeconomic conditions, making accurate volatility forecasting essential for traders, portfolio managers, and regulators. This study models the conditional volatility of BTC and ETH daily log returns using three models of GARCH family-the standard GARCH (1,1), the Exponential GARCH (EGARCH), and the Threshold GARCH (TGARCH) and compares their performance using the Akaike and Bayesian Information Criteria (AIC and BIC). Daily closing-price data for BTC and ETH were obtained from Yahoo finance. It has transformed into log returns and confirmed to be stationary via the Augmented Dickey-Fuller test. Autocorrelation analysis indicated no significant serial correlation in returns but strong volatility clustering in their squares, motivating an ARMA(0,0)-GARCH (1,1) mean-variance specification. Across both assets, models that accommodate asymmetric shocks and fat-tailed innovations outperformed the symmetric normal GARCH specification with TGARCH under a Student-t distribution performed best for both Bitcoin and Ethereum. Forecasts from the models point to a period of relative stabilization following historically volatile phases for both cryptocurrencies.},
        keywords = {Bitcoin, Ethereum, volatility modelling, GARCH, EGARCH, TGARCH, cryptocurrency risk.},
        month = {September},
        }