Sarrafshirazi, Raheleh (2025) High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models with Realised Semivariance and Jump Components. Masters thesis, Concordia University.
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Abstract
Bitcoin’s continuous trading, speculative nature, and high volatility create distinctive challenges for risk management and forecasting. This thesis examines how high-frequency realised volatility (RV) measures and Heterogeneous Autoregressive (HAR) models capture Bitcoin’s volatility dynamics and improve forecast accuracy. Adapting RV methods from equity markets, the analysis adds downside semivariance to address asymmetric negative returns and jump variation to capture price movements.
Using minute-level Bitcoin prices from, I compute RV from 5-minute returns and estimate four HAR variants—baseline HAR, HAR-RS, HAR-J, and HAR-RS-J. Models are re-estimated in a rolling window, and forecasts are evaluated with RMSE, MAE, and QLIKE. Robustness checks test stability under different data granularities and market regimes. A GARCH(1,1) benchmark provides a parametric comparison, with HAR variants outperforming it at short horizons, while GARCH exceeds performance at longer horizons.
Results show that HAR-type models capture Bitcoin’s long memory, volatility clustering, and asymmetry effectively. HAR-J delivers the most accurate day-ahead, while HAR-RS leads at weekly and monthly horizons due to persistent downside risk. At quarterly horizons, forecast accuracy converges across models as high-frequency information loses relevance.
This study extends RV–HAR modelling to cryptocurrency markets, revealing shorter volatility persistence and greater jump contributions than in equities. It identifies downside semivariance and continuous variation as robust predictors across different market conditions and offers horizon-specific tools—HAR-J for short-term risk management and HAR-RS for medium-term volatility planning.
| Divisions: | Concordia University > John Molson School of Business > Finance |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Sarrafshirazi, Raheleh |
| Institution: | Concordia University |
| Degree Name: | M.A. |
| Program: | Finance |
| Date: | August 2025 |
| Thesis Supervisor(s): | Kim, Kun Ho |
| Keywords: | Bitcoin Volatility, Realised Volatility, Heterogeneous Autoregressive Model, Semivariance, Jump Variation, High-Frequency Data, Volatility Forecasting, Cryptocurrency Markets, Bipower Variation, Risk Management |
| ID Code: | 995904 |
| Deposited By: | Raheleh Sarrafshirazi |
| Deposited On: | 04 Nov 2025 16:22 |
| Last Modified: | 04 Nov 2025 16:22 |
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