Value at Risk

Value at Risk (VaR) quantifies the potential loss in value of an asset or portfolio over a specific time horizon at a given confidence level, serving as a crucial tool in risk management across various fields. Current research emphasizes improving VaR estimations by incorporating market conditions, using machine learning techniques like variational inference and generative adversarial networks to model complex financial time series and account for uncertainty, and employing risk-sensitive algorithms such as Conditional Value-at-Risk (CVaR) optimization within reinforcement learning and stochastic shortest-path planning frameworks. These advancements enhance the accuracy and robustness of risk assessments, leading to better decision-making in finance, operations research, and safety-critical systems.