WORKSHOP ON DEPENDENCE ANALYSIS
26 and 27 of SEPTEMBER, 2025
Institute of Mathematics, Statistics and Scientific
Computation of the University of Campinas, Brazil
SATELLITE WORKSHOP OF
8th BRAZILIAN CONFERENCE ON STATISTICAL MODELING IN INSURANCE AND FINANCE
Title: The Implications of Dependence, Tail Dependence, and Bounds' Measures for Counterparty Credit Risk Pricing: An Empirical Analysis of the US Financial System
Here, we empirically investigate the counterparty credit risk of interest rate swap positions using the credit valuation adjustment (CVA) measure, with a particular focus on financial institutions. Specifically, we estimate the empirical implicit CVA risk associated with the derivatives positions that these institutions declare under the regulatory framework. Our analysis examines the potential dependence between the probability of default (PD) and exposure at default (EAD) by applying six tail dependence models-namely, a Basel III Committee independent model, and Gaussian, Student's t, Gumbel, Clayton, and wrong way risk copula approaches-to observed market data derived from interest rate swaption implied volatilities. The empirical results underscore that the choice of dependence specification between PD and EAD has significant implications for CVA estimation, particularly during periods of market stress. This study, based entirely on empirical evidence rather than simulation, provides valuable insights for regulators, financial institutions, and credit risk managers regarding the accurate measurement of counterparty risk.