Quantifying endogeneity of cryptocurrency markets

We construct a 'reflexivity' index to measure the activity generated endogenously within a market for cryptocurrencies. For this purpose, we fit a univariate self-exciting Hawkes process with two classes of parametric kernels to high-frequency trading data. A parsimonious model of both endogenous and exogenous dynamics enables a direct comparison with exchanges for traditional asset classes, in terms of identified branching ratios. We also formulate a 'Hawkes disorder problem,' as generalization of the established Poisson disorder problem, and provide a simulation-based approach to determining an optimal observation horizon. Our analysis suggests that Bitcoin mid-price dynamics feature long-memory properties, well explained by the power-law kernel, at a level of criticality similar to fiat-currency markets.

Automated summary

This article constructs a 'reflexivity' index to measure the activity generated endogenously within a market for cryptocurrencies and analyzes high-frequency trading data using a Hawkes process. The study finds that the mid-price dynamics of Bitcoin exhibit long-memory properties and can be well explained by a power-law kernel.