The purpose of this study is to evaluate Bitcoin’s return and volatility dynamics using advanced algorithmic analysis. It aims to integrate time-series momentum strategies with GARCH volatility modeling to enhance risk assessment and investment decision-making in cryptocurrency markets.
The research uses algorithmic analysis using Python coding to apply momentum strategies and GARCH volatility modeling for the period between 2017 and 2022. This integrated framework allows for the identification of and adaptation to market trends under varying conditions while also conducting AI-enhanced risk analysis to uncover patterns of extreme volatility in Bitcoin.
Key findings indicate that algorithmic momentum strategies can effectively generate excess returns and adapt to market trends. However, the study emphasizes the necessity of sophisticated risk management algorithms to mitigate the inherent volatility of Bitcoin. The volatility modeling confirms the persistent nature of volatility clustering within cryptocurrency markets, highlighting the importance of effective risk assessment.
This research contributes to the fintech literature by demonstrating the effectiveness of using algorithmic analysis in enhancing traditional financial analysis for cryptocurrency markets. It showcases how advanced techniques can provide superior insights into market dynamics compared to conventional methods.
