Multi-Horizon Bitcoin Volatility Forecasting Using Support Vector Machines - Hidayat, Syaiful; Faris Al Fatih, Mohammad; Parlika, Rizky (JURNAL FASILKOM, 2026)
This study uses Support Vector Regression (SVR) to forecast Bitcoin realized volatility across four timeframes (2h, 4h, 8h, and daily) and four forecast horizons using Binance OHLCV data from January 2023 to June 2026. Realized volatility is estimated using a 5-period rolling standard deviation of log returns. We construct 14 features from lagged volatility, returns, trading volume, RSI, MACD, Bollinger Bandwidth, and high-low spread. We evaluate linear and radial basis function (RBF) kernels using walk-forward validation with a 100-sample rolling window. To prevent look-ahead bias, the training window is adjusted for each forecast horizon so that only targets observable at the forecast time are included. Performance is evaluated using MAE, RMSE, MAPE, and R². The linear kernel outperforms the RBF kernel across all 16 timeframe-horizon combinations, achieving the highest R² of 0.5140 for daily volatility at the shortest horizon. Short horizons () generally produce positive R², whereas longer horizons () yield near-zero or negative R². The look-ahead bias correction produces only modest changes in performance, indicating that the initial bias had limited influence on the results. Permutation importance identifies lagged volatility as the dominant predictor, supporting volatility persistence. Overall, the findings demonstrate that linear SVR provides a simple, effective, and interpretable approach to Bitcoin volatility forecasting.