Detecting Bitcoin Market Regimes with Multifractal Scaling

Summary

The paper studies Bitcoin’s return structure across multiple time scales using tools for estimating multifractal properties. It describes return variance as following power-law relationships with time increments, characterized by volatility and a Hurst exponent. Since these parameters change over time, the authors introduce a generalized Hurst exponent to assess whether the multifractal behavior is captured adequately.

They use local power-law parameter estimates and goodness of fit to identify regime shifts, reporting that the method automatically detects dates associated with known Bitcoin market events. The paper also reports that, despite large price swings and nonstationarity, Bitcoin retains an orderly correlation structure over its full observed history. The summary provides no sample dates, event list, parameter estimates, or comparative validation, so these conclusions are limited to the analyzed period and method described.

Key ideas

• Bitcoin returns exhibit multiscale correlations described through power-law scaling of variance with time increments.
• Volatility and the Hurst exponent characterize the scaling behavior, and both can vary over time.
• A generalized Hurst exponent is proposed to assess the fit of multifractal behavior.
• Local power-law estimates and goodness of fit are used to detect regime shifts.
• The reported analysis finds event-associated shifts and an orderly long-run correlation structure over the studied history.

Tags

# Chaos and Order in the Bitcoin Market


# Chaos and Order in the Bitcoin Market









The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.