This paper introduces the application of Deep Reinforcement Learning (#drl) in #alm, addressing limitations of traditional methods reliant on human judgement. The findings highlight the potential of DRL to enhance #riskmanagement outcomes for #insurers, #banks, #pensionfunds, and #assetmanagers, providing improved adaptability to changing market conditions.
This paper explores optimal insurance contracting for a decision maker facing ambiguous loss distributions. Using a p-Wasserstein ball...
This research examines how ESG performance impacts default probability (PD) in life and non-life insurance firms. Findings show that...
The study assesses the impact of Europe's Single Supervisory Mechanism on banks' balance sheets, finding that centrally supervised banks...
Comentarios