The impact of global tariff pressures and geopolitical conflicts on corporate default rates

Abstract

The escalating intensity of global tariff pressures and geopolitical conflicts in the post-pandemic era has engendered a complex web of macroeconomic dislocations with profound implications for corporate financial stability. Despite a burgeoning body of literature examining trade policy uncertainty and geopolitical risk in isolation, a coherent conceptual framework that systematically links these macro-level triggers to corporate default rates remains conspicuously absent from the scholarly discourse. This paper addresses that lacuna by developing an integrated conceptual framework that maps the pathways through which Global Tariff Pressure Indices (GTPI) and Geopolitical Conflict Indices (GCI) propagate through four transmission channels, namely cost pressure, demand shock, financial market volatility, and supply chain disruption, to elevate firm-level Corporate Default Risk (CDR). Grounded in the structural credit risk tradition of Merton (1974) and its extensions, including the KMV model and the Altman Z-score, the framework synthesises insights from international trade theory, macrofinancial economics, and geopolitical risk scholarship (Caldara & Iacoviello, 2022; Baker, Bloom & Davis, 2016). Six formal propositions are advanced, positing that tariff-induced cost escalation, demand contraction, exchange rate volatility, and supply chain fragmentation each independently and jointly amplify corporate default probabilities, with moderating effects from firm size, sectoral exposure, geographic diversification, and institutional quality. The framework carries significant implications for corporate managers in recalibrating hedging strategies and supply chain architecture; for investors and credit rating agencies in integrating geopolitical and tariff risk into credit assessments; and for policymakers in designing counter-cyclical fiscal and trade diplomacy mechanisms. The paper concludes by identifying fertile avenues for empirical validation, including panel data analyses, event study methodologies, and machine learning applications to default prediction.