arXiv research

The paper analyzes risk estimation methods and derives bounds for OCE risk.

New multivariate risk measures improve on univariate OCE methods.

Study risk-sensitive reinforcement learning with optimized certainty equivalents.

The paper studies risk-sensitive learning schemes and provides learning bounds for empirical OCE minimizers.

New method for dynamic valuation in markets with random endowments.

Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including t…

This work addresses time inconsistency in risk measures and develops a dynamic programming principle for risk minimization problems.

Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from t…

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

Solves Merton's investment-consumption problem with certainty equivalent approach.

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

Study shows certainty equivalent policy minimizes regret in continuous-time systems.

Study on implied certainty equivalent rates in financial markets and electric vehicles.

For incomplete preference relations that are represented by multiple priors and/or multiple -- possibly multivariate -- utility functions, we define a certainty equivalent as well as the utility buy and sell prices and indifference price bounds as set-valued functions of the claim. Furthermore, we motivate and introduc…

Introduces new performance measures using scaled utility functions.

CEFOL uses deep learning for dynamic programming with recursive utility.

Study scaling limits for option pricing in trinomial models.

Study risk-sensitive market making with entropy regularization for better quote control.

We consider the class of risk measures associated with optimized certainty equivalents. This class includes several popular examples, such as CV@R and monotone mean-variance. Numerical schemes are developed for the computation of these risk measures using Fourier transform methods. This leads, in particular, to a very …

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ) control problems with unknown transition dynamics. We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system …

We consider the problem of optimal risk sharing in a pool of cooperative agents. We analyze the asymptotic behavior of the certainty equivalents and risk premia associated with the Pareto optimal risk sharing contract as the pool expands. We first study this problem under expected utility preferences with an objectivel…

Study optimal investment decisions for diverse risk-tolerant agents.

We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract a progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the foll…

The paper develops robust risk measures for uncertain loss positions.

The study constructs Yamabe operators on OC manifolds and proves their properties.

Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.

This paper studies the optimal risk-averse timing to sell a risky asset. The investor's risk preference is described by the exponential, power, or log utility. Two stochastic models are considered for the asset price -- the geometric Brownian motion and exponential Ornstein-Uhlenbeck models -- to account for, respectiv…

Deep learning solves dynamic programming with recursive utility.

Optimized portfolio management with dynamic market regimes using RL and OC learning.

This work models GHG offset credit markets to find optimal strategies for market participants.

Optimal control strategy uses random noise to adaptively control systems with unknown parameters.

Proposes a new method to rank risky investments based on Omega measure.

Submodularity is studied for convex risk measures, including Expected Shortfall.

This paper shows CEM is a special case of TTM, leading to new proofs and improved sample complexity bounds.

New bounds for adaptive control in high dimensions without fixed state space.

A new method uses active learning to monitor industrial processes more accurately.

This work uses a scalable approach to identify partially observed nonlinear systems.

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and t…

We consider the optimal investment problem when the traded asset may default, causing a jump in its price. For an investor with constant absolute risk aversion, we compute indifference prices for defaultable bonds, as well as a price for dynamic protection against default. For the latter problem, our work complements S…

In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we …

New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.

Scores measure certainty and doubt in classification predictions.

Investors optimize equity and CDS trading to mitigate default risk.

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

This paper tackles adaptive control of unknown Markov jump systems with sample complexity and regret bounds.

We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well …

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes …

A framework for cost of belief revision in uncertain agents.