Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk

Paper: arXiv 2402.06032

Abstract

Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR’s key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model’s superior resilience and invariant predictive power, essential for navigating the complexities of today’s ever-evolving financial markets.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a complex causal network framework with advanced statistical methods (math complexity high), while demonstrating robustness through simulation studies and application to Forex market data (empirical rigor moderate to high).

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Risk Prediction Amidst Market Turbulence"] --> B["Data Collection<br>Forex Market Data"]
  B --> C["Methodology: Causal-NECO VaR<br>Network Construction & Causal Inference"]
  C --> D["Computational Process:<br>Volatility & Spillover Analysis"]
  D --> E{"Key Outcomes"}
  E --> F["Predictive Accuracy<br>Superior to Correlation-Based Models"]
  E --> G["Invariance & Resilience<br>Robust Against Market Shocks"]
  E --> H["Clearer Risk Assessment<br>Directional Influence Mapping"]

Cited References (420)

1. Big data, data science, and causal inference: A primer for clinicians (2021)
2. On causal inference in the presence of interference (2012)
3. Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (2007) — link
4. Quantum Monte Carlo: Origins, Development, Applications (2007)
5. The interbank market after August 2007: what has changed, and why? (2011)
6. Statistics and causal inference (1986)
7. Causal inference, path analysis and recursive structural equations models (1988)
8. Atkins’ Physical Chemistry (2002) — link

0 of 420 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.