arXiv research

New risk measures incorporate economic states to assess crude oil derivatives.

ERTS uses Thompson sampling for Gaussian entropic risk bandits, achieving regret bounds.

Efficiently computes optimal policies for Entropic Risk Measures.

Researchers calculated EVaR for various distributions using Lambert function.

New method corrects bias in estimating entropic risk for better decision-making.

Paper proposes risk-averse reinforcement learning algorithms.

Using elements from the theory of ergodic backward stochastic differential equations (BSDE), we study the behavior of forward entropic risk measures. We provide their general representation results (via both BSDE and convex duality) and examine their behavior for risk positions of long maturities. We show that forward …

The paper mentioned in the title introduces the entropic value at risk. I give some extra comments and using the general theory make a relation with some commonotone risk measures.

Proposes a method to generate counterfactuals for ensemble models using entropic risk measures.

Study risk-sensitive reinforcement learning with entropic risk measures and generative models.

Paper introduces Lambda EVaR, a new risk measure.

Investment strategy optimizes risk using a specific risk measure.

This paper introduces new risk measures for evaluating losses with varying time horizons.

The paper develops a new approach to conditional risk measures using modular convex analysis.

Improved sample complexity for identifying best policies in risk-sensitive reinforcement learning.

New risk measures control subgroup imbalances, improving PAC-Bayesian bounds.

Study gap-dependent regret bounds for risk-sensitive RL.

We introduce the entropic measure transform (EMT) problem for a general process and prove the existence of a unique optimal measure characterizing the solution. The density process of the optimal measure is characterized using a semimartingale BSDE under general conditions. The EMT is used to reinterpret the conditiona…

Solves risk-sensitive investment via duality, entropic regularization, and RL.

An Entropic Dynamics of exchange rates is laid down to model the dynamics of foreign exchange rates, FX, and European Options on FX. The main objective is to represent an alternative framework to model dynamics. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where…

New risk measure considers horizon risk and interest rate uncertainty.

This paper is devoted to study the optimal portfolio problem. Harry Markowitz's Ph.D. thesis prepared the ground for the mathematical theory of finance. In modern portfolio theory, we typically find asset returns that are modeled by a random variable with an elliptical distribution and the notion of portfolio risk is d…

We generalize Quasi-Linear Means by restricting to the tail of the risk distribution and show that this can be a useful quantity in risk management since it comprises in its general form the Value at Risk, the Tail Value at Risk and the Entropic Risk Measure in a unified way. We then investigate the fundamental propert…

In this paper, we provide a representation theorem for dynamic capital allocation under It{ô}-L{é}vy model. We consider the representation of dynamic risk measures defined under Backward Stochastic Differential Equations (BSDE) with generators that grow quadratic-exponentially in the control variables. Dynamic capital …

We develop an entropic framework to model the dynamics of stocks and European Options. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. The objective of the paper is to lay down an alternative framework for modeling dynamic…

Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman and Xu to the dynamic ca…

Proposes a new derivative concept for nonlinear DRO problems.

The entropic value-at-risk (EVaR) is a new coherent risk measure, which is an upper bound for both the value-at-risk (VaR) and conditional value-at-risk (CVaR). As important properties, the EVaR is strongly monotone over its domain and strictly monotone over a broad sub-domain including all continuous distributions, wh…

Introduces entropic tribrackets and their applications in link distinguishing.

New algorithms reduce risk in reinforcement learning with provable regret bounds.

Generalizes risk sharing models to a continuum of agents.

We consider insurance derivatives depending on an external physical risk process, for example a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference…

Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.

We define link and graph invariants from entropic magmas modeling them on the Kauffman bracket and Tutte polynomial. We define the homology of entropic magmas. We also consider groups that can be assigned to the families of compatible entropic magmas.

We derive generalization and excess risk bounds for neural nets using a family of complexity measures based on a multilevel relative entropy. The bounds are obtained by introducing the notion of generated hierarchical coverings of neural nets and by using the technique of chaining mutual information introduced in Asadi…

New privacy framework tailored to specific data distributions.

The paper analyzes stability and convergence rates of entropic and Sinkhorn potentials.

Study of Gaussian distributions using entropic Gromov-Wasserstein and inner product Gromov-Wasserstein.

Sharp bounds found for various risk measures using generalized FGM copulas.

Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…

The paper studies risk-sensitive MDPs with recursive risk measures.

Improved risk-sensitive RL with exponential Bellman equation and better regret bounds.

A new framework tightens risk measure confidence bounds.

In this paper we derive robust super- and subhedging dualities for contingent claims that can depend on several underlying assets. In addition to strict super- and subhedging, we also consider relaxed versions which, instead of eliminating the shortfall risk completely, aim to reduce it to an acceptable level. This yie…

New concept of partial law invariance connects decision theory and financial risk management.

Improved neural framework for scaling entropic MOT with significant computational gains.

Optimizes portfolios with GM returns using convex optimization.

Optimized certainty equivalents (OCEs) is a family of risk measures widely used by both practitioners and academics. This is mostly due to its tractability and the fact that it encompasses important examples, including entropic risk measures and average value at risk. In this work we consider stochastic optimal control…