Vector Autoregression in Cryptocurrency Markets: Unraveling Complex Causal Networks

ArXiv ID: 2308.15769 “View on arXiv”

Authors: Unknown

Abstract

Methodologies to infer financial networks from the price series of speculative assets vary, however, they generally involve bivariate or multivariate predictive modelling to reveal causal and correlational structures within the time series data. The required model complexity intimately relates to the underlying market efficiency, where one expects a highly developed and efficient market to display very few simple relationships in price data. This has spurred research into the applications of complex nonlinear models for developed markets. However, it remains unclear if simple models can provide meaningful and insightful descriptions of the dependency and interconnectedness of the rapidly developed cryptocurrency market. Here we show that multivariate linear models can create informative cryptocurrency networks that reflect economic intuition, and demonstrate the importance of high-influence nodes. The resulting network confirms that node degree, a measure of influence, is significantly correlated to the market capitalisation of each coin ($ρ=0.193$). However, there remains a proportion of nodes whose influence extends beyond what their market capitalisation would imply. We demonstrate that simple linear model structure reveals an inherent complexity associated with the interconnected nature of the data, supporting the use of multivariate modelling to prevent surrogate effects and achieve accurate causal representation. In a reductive experiment we show that most of the network structure is contained within a small portion of the network, consistent with the Pareto principle, whereby a fraction of the inputs generates a large proportion of the effects. Our results demonstrate that simple multivariate models provide nontrivial information about cryptocurrency market dynamics, and that these dynamics largely depend upon a few key high-influence coins.

Keywords: financial networks, multivariate models, cryptocurrency market, causal representation, high-influence nodes, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper employs Vector Autoregression (VAR) and statistical network analysis, which involves moderate mathematical complexity, but focuses heavily on empirical data application with specific datasets (261 cryptocurrencies, hourly returns) and statistical metrics (Spearman correlation, Gini coefficient), indicating a street-trader approach.

  flowchart TD
    A["Research Goal: Assess if simple multivariate models can describe cryptocurrency market dynamics?"] --> B["Methodology: Vector Autoregression & Causal Network Inference"]
    B --> C["Data: Multivariate Price Series of Speculative Crypto Assets"]
    C --> D["Computational Process: Model Complexity vs. Market Efficiency Analysis"]
    D --> E{"Linear Model Application"}
    E -->|Success| F["Outcome 1: Informative Networks reflecting Economic Intuition"]
    E -->|Reductive Experiment| G["Outcome 2: Pareto Principle confirmed<br/>Small portion of nodes drives market effects"]
    F --> H["Key Finding: Node degree correlates with market cap<br/>(ρ=0.193) & High-Influence Nodes identified"]
    G --> H
    H --> I["Conclusion: Simple linear models reveal inherent complexity<br/>& prevent surrogate effects in crypto markets"]