We investigate the role of market microstructure metrics in predicting price dynamics for five cryptocurrencies. We show that measures of liquidity and price discovery have predictive power for price dynamics measures used in electronic market making, dynamic hedging strategies, and volatility estimation. We identify own market and cross-market effects for Roll measures and VPINs in BTC and ETH. Our results change little during crypto winter or the 2022 change in interest regimes. Market dynamics of cryptocurrencies are similar to those of futures but exhibit greater toxicity. Our findings are relevant for proposals regarding the appropriate regulatory structure for digital currencies.

Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.