Statistical Properties and Power Analysis of Divergence Measures for Credit Risk Model Monitoring
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摘要
This paper derives the exact chi-square distributions and benchmark values for Jensen–Shannon Divergence (JSD) and Kullback–Leibler Divergence (KLD), and benchmarks them against PSI in monitoring distribution shifts of credit default probabilities generated by Merton, Merton with Jump, and Stochastic Volatility with Jump models. Key findings: JSD shows superior Type I error control near 5% but sacrifices power at small samples; KLD offers higher sensitivity; PSI tends to have higher power in larger samples. The work provides practical guidance on binning, sample-size requirements (min(n,m) ≥ 400), and when to use permutation/bootstrap corrections for sparse data, with empirical validation showing consistent discrimination of PD distributions across models [page::1]
