Bitcoin Price Volatility Analysis and Risk Prediction Based on GARCH Model

As a highly volatile cryptocurrency, Bitcoin's accurate price volatility analysis and risk prediction are crucial for market risk management. This study aims to evaluate the GARCH model's ability to characterize Bitcoin's price fluctuation dynamics and its effectiveness in predicting value at risk. Using the 20202025 Bitcoin daily yield data, the conditional volatility is estimated based on the Student-t distribution and the dynamic VaR is calculated by establishing the GARCH model. The results of this paper show that Bitcoin fluctuations show high persistence and thick tail characteristics, and the dynamic VaR out-of-sample breakout rate is 4.7%, which is close to the theoretical value. This research result can provide effective warnings before most major risk events, but underestimates extreme tail risks. In summary, the empirical results of this study provides practical risk monitoring tools for the cryptocurrency market and points the way for improved models such as fusion extreme value theory.