arXiv research

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…

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

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 …

Study risk-sensitive reinforcement learning with optimized certainty equivalents.

New multivariate risk measures improve on univariate OCE methods.

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.

The paper develops robust risk measures for uncertain loss positions.

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 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…

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

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…

New method for dynamic valuation in markets with random endowments.

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.

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 …

Scores measure certainty and doubt in classification predictions.

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

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 …

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…

A framework for cost of belief revision in uncertain agents.

This paper tackles over-certainty in test-time adaptation models, proposing a solution to improve calibration.

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

We consider the problem of online adaptive control of the linear quadratic regulator, where the true system parameters are unknown. We prove new upper and lower bounds demonstrating that the optimal regret scales as $\widetildeΘ({\sqrt{d_{\mathbf{u}}^2 d_{\mathbf{x}} T}})$, where $T$ is the number of time steps, $d_{\m…

Investment strategy optimizes risk using a specific risk measure.

We consider a multi-stock continuous time incomplete market model with random coefficients. We study the investment problem in the class of strategies which do not use direct observations of the appreciation rates of the stocks, but rather use historical stock prices and an a priory given distribution of the appreciati…

We consider a model in which a trader aims to maximize expected risk-adjusted profit while trading a single security. In our model, each price change is a linear combination of observed factors, impact resulting from the trader's current and prior activity, and unpredictable random effects. The trader must learn coeffi…

Optimal portfolios are formed by combining momentum, size, and volatility characteristics, enhancing utility for all investors.

In this article we consider a special case of an optimal consumption/optimal portfolio problem first studied by Constantinides and Magill and by Davis and Norman, in which an agent with constant relative risk aversion seeks to maximise expected discounted utility of consumption over the infinite horizon, in a model com…

We study the dynamic indifference pricing with ambiguity preferences. For this, we introduce the dynamic expected utility with ambiguity via the nonlinear expectation--G-expectation, introduced by Peng (2007). We also study the risk aversion and certainty equivalent for the agents with ambiguity. We obtain the dynamic …