Identifying Volatility Regimes in Bitcoin Prices

We apply clustering techniques to volatility and market sentiment measurements of historical Bitcoin prices in the aim of identifying hidden structural patterns separating different regions of our data. Using these regimes should help a fund allocate between trading strategies depending on market conditions, based on relatively simple market measurements. K-means, Ward, complete-linkage Agglomerative and Birch Clustering was used to separate the data. The resulting clusters were used as state vectors in a Markov Chain model, and used to predict the market’s state in the short-term horizon. Lastly, we provide an improvement to our one time-step-ahead forecast using historical results and the conditional probability framework.