This paper introduces the concept of entropic value-at-risk (EVaR), a new coherent risk
measure that corresponds to the tightest possible upper bound obtained from the Chernoff …

In the past few decades the financial industry has experienced several economic cycles.
There were periods of rapid economic growth interspersed with periods of economic …

We propose a novel framework to solve risk-sensitive reinforcement learning problems
where the agent optimizes time-consistent dynamic spectral risk measures. Based on the …

We develop an approach for solving time‐consistent risk‐sensitive stochastic optimization
problems using model‐free reinforcement learning (RL). Specifically, we assume agents …

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state
and action spaces and unbounded cost. We treat both finite and infinite planning horizons …

The aim of this work is to study risk measures generated by distortion functions in a dynamic
discrete time setup and to investigate the corresponding dynamic coherent acceptability …

Since risky positions in multivariate portfolios can be offset by various choices of capital
requirements that depend on the exchange rules and related transaction costs, it is natural …

We introduce a framework for quantifying propagation of uncertainty arising in a dynamic
setting. Specifically, we define dynamic uncertainty sets designed explicitly for discrete …

We investigate a discrete-time mean-risk portfolio selection problem, where risk is measured
by the conditional value-at-risk (CVaR). A substantial challenge is the combination of a time …

In this paper, we consider a finite-horizon Markov decision process (MDP) for which the
objective at each stage is to minimize a quantile-based risk measure (QBRM) of the …