A Look at Financial Dependencies by Means of Econophysics and Financial Economics

Paper: arXiv 2302.08208

Authors: M. Raddant, T. Di Matteo

Abstract

This is a review about financial dependencies which merges efforts in econophysics and financial economics during the last few years. We focus on the most relevant contributions to the analysis of asset markets’ dependencies, especially correlational studies, which in our opinion are beneficial for researchers in both fields. In econophysics, these dependencies can be modeled to describe financial markets as evolving complex networks. In particular we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial data sets. In financial economics these dependencies can describe asset comovement and spill-overs. In particular, several models are presented that show how network and factor model approaches are related to modeling of multivariate volatility and asset returns respectively. Finally, we sketch out how these studies can inspire future research and how they contribute to support researchers in both fields to find a better and a stronger common language.

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 5.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper provides a solid survey bridging econophysics and financial economics, focusing on correlation networks and dependency modeling. While primarily a review rather than a novel empirical study, it effectively synthesizes complex network theory and multivariate volatility frameworks with strong clarity and thoroughness.

Research Flowchart

  flowchart TD
    A["Research Goal: Integrate Econophysics & Financial Economics to Model Financial Dependencies"] --> B["Data Inputs: Complex Multivariate Financial Datasets (Equities & Volatility)"]
    B --> C["Methodology: Merge Network Theory & Econometric Models"]
    C --> D["Computational Process: Apply Information Filtering Networks & Multivariate Factor Models"]
    D --> E["Findings: Network Topology Captures Asset Comovements & Volatility Spillovers"]
    E --> F["Outcomes: Unified Framework for Cross-Disciplinary Research & Market Modeling"]