Financial risk assessment under climate policy uncertainty : A Bayesian learning framework for equity markets

Abstract

A forward-looking framework is proposed to assess financial risks arising from climate policy uncertainty in equity markets. In contrast to traditional stress-testing methods based on fixed scenario paths, transition risk is modelled as a sequence of stochastic carbon price shocks, the frequency of which evolves over time, through a Bayesian learning process reflecting the revision of investor beliefs. This approach captures the dynamic nature of regulatory uncertainty and its impact on asset valuations. To evaluate financial vulnerability, a novel metric — the market compensation probability — is introduced. This metric quantifies the likelihood that the underlying dynamics of financial market prices offset climate-induced financial losses. The framework is empirically applied to the Moroccan equity market, an emerging market context characterised by significant limitations in the availability of relevant climate-related financial data. Results indicate that the overall market exhibits moderate exposure due to its relatively low average carbon intensity, while the electricity sector, which is characterised by high emissions, faces persistent financial erosion. These findings highlight the importance of integrating climate policy uncertainty into financial risk assessments. By identifying sectors most at risk and quantifying downside exposures, the model offers valuable guidance for risk managers, institutional investors, and regulators. It supports the development of climate-aware investment strategies and promotes capital reallocation towards lower-carbon assets, thereby enhancing portfolio resilience and advancing climate transition goals. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.