Project 3: Regime switches and optimal asset allocation in climate finance and insurance
Work Package 1:
This work package develops regime-switching models linking regulatory uncertainty to changes in asset price dynamics. We study optimal investment strategies when the drift, timing, or consequences of regulatory decisions are uncertain. The analysis incorporates Bayesian learning about unknown policy effects and explores the role of ESG preferences by modeling “green” and “brown” assets. Investors update beliefs about policy shifts (e.g., carbon taxation or ESG ratings) through observed price signals, facing both risk and ambiguity. The objective is to quantify welfare effects, learning benefits, and portfolio adjustments resulting from uncertain regulatory regimes, thereby explaining shifts toward sustainable investments under policy uncertainty.
Development of RS models for asset price dynamics
Work Package 2:
WP2 investigates how information about future regulatory decisions influences asset allocation and welfare. We compare pre-commitment and dynamically consistent strategies to measure the value of learning and the cost of uncertainty. We also study situations where the regulator controls information flow, determining when and how to disclose policy decisions. The analysis quantifies investors’ willingness to pay for information and evaluates welfare improvements from partial information release. Furthermore, we extend the model to account for uncertainty about prior beliefs and ambiguity aversion, exploring how additional regulatory signals—such as political debates or announcements—affect expectations and market stability.
Value and release of regulatory information
Work Package 3:
Regime-switching investment
WP3 examines how regulatory constraints—such as Value-at-Risk or Expected Shortfall limits—change across regimes. We analyze how these dynamic constraints influence optimal investment and financial stability. In downturns, stricter constraints are imposed, modeled as regime switches in risk restrictions. Using approaches from Basak and Shapiro (2001) and Escobar-Anel (2022), we quantify the trade-off between investor welfare loss and systemic risk reduction. The regulator’s perspective is included by studying how constraint design affects aggregate stability and political costs. Overall, WP3 provides theoretical and policy insights into how adaptive regulation can mitigate systemic risk while balancing investor utility.
In this project, we focus on the specific use of regime switching (RS) models to analyze the implications of regulatory uncertainty for both asset allocation and the efficiency of the objective of the regulator in climate finance and insurance. Work Package 1 is dedicated to mathematical model setups of asset allocation problems which are stylized to capture the basic impacts of regulatory decisions. The main focus of Work Package 2 concerns the value of different information structures and the optimal release of regulatory information. Work Package 3 accounts for additional regime switches in regulatory risk constraints which are imposed on the optimization problems of the investors. Nicole Branger and Antje Mahayni are involved in all three WPs. For WP 2 (3) Nicole Bäuerle (An Chen) assumes a consultative role. The research objectives lead to the following Work Packages:
(consultative role)
Principal Investigators:
