Nos derniers articles

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 the #reputationalrisk associated with #esg investments and provides a formal theoretical valuation system for ESG reputation. The authors argue that ESG criteria adoption has multiple positive dimensions and outcomes, but the analysis of the #risks related to #sustainability is uncommon. They model ESG reputational risk using paradigms of #behaviouralfinance, defining it by subjective probabilities framed in a #probability function based on potential trustees' preferences. The paper highlights the need for accurate evaluation of reputational risks related to ESG investments by firms and other institutions, including #insurance companies and #pensionfunds.