Scenario Matching for Credit Dependence Stress Tests: Temporal correlation constraints and empirical risk bounds

Naoufal Nifa ()
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Naoufal Nifa: Independent Researcher, BNP-Paribas

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Abstract: We couple two sets of simulated market and credit paths by minimizing absolute temporal-correlation error relative to a prescribed target. Each market path receives exactly one credit path, preserving both empirical distributions of complete scenarios. A perfect-matching test identifies whether a uniform correlation-error tolerance is feasible and determines its smallest attainable value. Further assignments give exact empirical mean-loss and exceedance-probability bounds; quantile bounds follow by binary search over eligible loss thresholds. A synthetic bond-forward credit stress test illustrates the method. The experiments report matching precision, empirical score tails and exposure-grid error, and measure matching and mean-loss computation. The method is independent of the scenario generator and provides a reproducible way to examine dependence assumptions in existing risk engines. Tail measures concern default-probability-weighted credit scores. Accompanying reproducibility package (Python code, synthetic scenario libraries, permutations, and benchmarks) is attached in the ZIP archive.

Keywords: scenario coupling temporal correlation credit stress testing assignment wrong-way risk empirical tail bounds; scenario coupling; temporal correlation; credit stress testing; assignment; wrong-way risk; empirical tail bounds (search for similar items in EconPapers)
Date: 2026-10-24
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-05802781

DOI: 10.5281/zenodo.23405059

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