Credit Valuation Adjustment in Financial Networks

Paper: arXiv 2305.16434

Authors: Irena Barjašić, Stefano Battiston, Vinko Zlatić

Abstract

Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to clarify when and how these network effects matter for CVA, in particular in the presence of correlation among counterparties defaults. We provide an approximate analytical solution for the default probabilities. This solution allows for identifying conditions on key parameters such as network degree, leverage and correlation, where network effects yield large differences in CVA (e.g. above 50%), and thus relevant for practical applications. Moreover, we find evidence that network effects induce a multi-modal distribution of CVA values.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces a mathematically sophisticated analytical model to evaluate CVA within financial networks under counterparty default correlation. However, it relies predominantly on theoretical modeling and parameter sensitivity rather than extensive empirical backtesting with real market data.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess Impact of Network Effects & Default Correlation on CVA] --> B[Model Development: Financial Network of Interconnected Derivatives Contracts]
    B --> C[Inputs: Network Degree, Leverage & Counterparty Default Correlation]
    C --> D[Computational Step: Approximate Analytical Solution for Default Probabilities]
    D --> E[Outcome 1: Identified Parameter Conditions Causing >50% CVA Differences]
    D --> F[Outcome 2: Discovered Multi-Modal Distribution of CVA Values]