European Sovereign Debt Control through Reinforcement Learning

Tato Khundadze^*Willi Semmler

• The New School, New York City, United States

The resilience of economic systems depends mainly on coordination among key stakeholders during macroeconomic or external shocks, while a lack of coordination can lead to financial and economic crises. The paper builds on the experience of global and regional shocks, such as the Eurozone crises of 2009-2012 and the economic disruption resulting from COVID-19 starting in 2020. The paper demonstrates the importance of cooperation in monetary and fiscal policies during emergencies to address macroeconomic non-resilience, particularly focusing on public debt management. The Euro area is chosen as the sample for testing the models presented in the paper, given that its resilience is heavily dependent on cooperation among different actors within the region. The shocks affecting nations within the European Union are asymmetric, and the responses to these shocks require coordination, considering heterogeneous economic structures, levels of economic development, and policies. The paper builds on the previous work of Semmler and Haider (2018) on cooperative behavior and Fiscal Policies in the Euro Area, which uses Nonlinear Model Predictive Control (NMPC) to trace the path of key macroeconomic variables (inflation rate, interest rate, output gap, government gross debt, and government net lending) and their dynamics under non-cooperative and cooperative scenarios. This paper further extends this type of work by applying Deep Reinforcement Learning to modeling the dynamics of the aforementioned variables by testing the performance of Soft Actor Critic (SAC) algorithm in a macroeconomic environment.