Correlation without Factors in Retail Cryptocurrency Markets

TL;DR

This paper introduces a model-free statistic to analyze the correlation structure of retail cryptocurrency returns, revealing high average correlations and supporting an isotropic correlation model over factor models, with stable cross-sectional properties.

Contribution

It presents a novel, distribution-free method to characterize correlation without factors, applied to retail cryptocurrency markets, demonstrating high correlations and model stability.

Findings

Average pairwise correlation around 60%

Data supports isotropic correlation model

Correlation structure remains stable over time

Abstract

A simple model-free and distribution-free statistic, the functional relationship between the number of "effective" degrees of freedom and portfolio size, or N*(N), is used to discriminate between two alternative models for the correlation of daily cryptocurrency returns within a retail universe of defined by the list of tradable assets available to account holders at the Robinhood brokerage. The average pairwise correlation between daily cryptocurrency returns is found to be high (of order 60%) and the data collected supports description of the cross-section of returns by a simple isotropic correlation model distinct from a decomposition into a linear factor model with additive noise with high confidence. This description appears to be relatively stable through time.