Environmental CVA with KL-Robust Wrong-Way Risk

Abstract

Although climate and nature related scenario analysis is increasingly important in finance, operational implementations remain limited for translating long horizon environmental scenarios into counterparty credit risk measures used in pricing and regulatory capital. We propose an environmental valuation adjustment framework for CVA with three components: (i) a scenario to credit translation that maps environmental scenario drivers into hazard rates; (ii) nature specific tail generators that quantify model risk in scenario generation; and (iii) a distributionally robust wrong way risk bound based on Kullback Leibler (KL) divergence. We compute climate CVAs using transition scenarios and nature CVAs using biodiversity indicators. Our results show that nature CVAs can vary materially across alternative ecosystem generators, highlighting an additional source of model uncertainty.