Applications of Hidden Markov Models in Detecting Regime Changes in Bitcoin Markets

This study explores the identification and assessment of regime shifts in Bitcoin markets through the application of advanced statistical models, namely HMMs, MSMs, and Threshold Models. The analysis utilizes key financial indicators including market capitalization, volatility, trading volume, and historical Bitcoin price data, along with statistical measures such as mean, minimum, and maximum values to enhance the detection of market patterns. Distinctions are made between bullish (sustained price increases exceeding 20%), bearish (sustained price declines exceeding 20%), and neutral (periods of low volatility and sideways movement) market regimes. HMMs provide predictive insights into market transitions, MSMs are employed to capture structural regime changes, and the Threshold Model identifies significant price behaviors. The findings indicate that HMMs outperform the other models in forecasting regime shifts, particularly in detecting transitions among bullish, bearish, and neutral phases information crucial for strategic trading decisions. Unlike traditional models, HMMs effectively accommodate the non-stationary characteristics of Cryptocurrency markets by incorporating variables such as market sentiment, regulatory developments, technological advancements, and macroeconomic conditions. The study presents a comprehensive HMM-based framework for regime detection and market forecasting that supports traders in optimizing entry, exit, and holding strategies to maximize profits while managing risk. Furthermore, the Viterbi algorithm is employed to evaluate the accuracy and reliability of HMM-based forecasts, confirming the robustness of HMMs in modeling and predicting complex market regimes.