Invisible Frontiers: Robust and Risk-Sensitive Financial Decision-Making within Hidden Regimes

In this dissertation, we delve into the exploration of robust and risk-sensitive strategies for financial decision-making within hidden regimes, focusing on the effective portfolio management of financial market risks under uncertain market conditions. The study is structured around three pivotal topics, that is, Risk-sensitive Policies for Portfolio Management, Robust Optimal Life Insurance Purchase and Investment-consumption with Regime-switching Alpha-ambiguity Maxmin Utility, and Robust and Risk-sensitive Markov Decision Process with Hidden Regime Rules. In Risk-sensitive policies for Portfolio Management, we propose two novel Reinforcement Learning (RL) models. Tailored specifically for portfolio management, these models align with investors’ risk preference, ensuring the strategies balance between risk and return. In Robust Optimal Life Insurance Purchase and Investment-consumption with Regime-switching Alpha-ambiguity Maxmin Utility, we introduce a pre-commitment strategy that robustly navigates insurance purchasing and investment-consumption decisions. This strategy adeptly accounts for model ambiguity and individual ambiguity aversion within a regime-switching market context. In Robust and Risk-sensitive Markov Decision Process with Hidden Regime Rules, we integrate hidden regimes into Markov Decision Process (MDP) framework, enhancing its capacity to address both market regime shifts and market fluctuations. In addition, we adopt a risk-sensitive objective and construct a risk envelope to portray the worst-case scenario from RL perspective. Overall, this research strives to provide investors with the tools and insights for optimal balance between reward and risk, effective risk management and informed investment choices. The strategies are designed to guide investors in the face of market uncertainties and risk, further underscoring the criticality of robust and risk-sensitive financial decision-making.