Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression

ArXiv ID: 2408.12210 “View on arXiv”

Authors: Unknown

Abstract

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don’t fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.

Keywords: Quantile Regression, Causality Networks, Tail Interactions, Cryptocurrency, Distributional Causality, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical modeling techniques including piecewise linear embeddings and quantile regression, involving significant mathematical formulation; the methodology is applied to a large-scale, real-world dataset (260 cryptocurrencies) with detailed empirical results, though specific backtesting code or robustness checks are not explicitly shown in the excerpt.

  flowchart TD
    A["Research Goal<br>Detecting distribution-level causality<br>beyond mean-response models"] --> B["Methodology<br>Quantile Regression + Piecewise Linear<br>Embedding for Tail Interactions"]
    B --> C["Data Input<br>260 Cryptocurrency<br>Return Series"]
    C --> D["Computational Process<br>Inference of Cross-Quantile<br>Causality Matrices"]
    D --> E{"Analysis of<br>Network Topology"}
    E --> F1["Key Finding 1<br>Significant Tail-Tail<br>Causal Effects"]
    E --> F2["Key Finding 2<br>Strong Causal<br>Asymmetry"]
    E --> F3["Key Finding 3<br>Bitcoin as Primary<br>Network Influencer"]