arXiv research

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

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

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

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

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…

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 …

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

We consider families of strongly consistent multivariate conditional risk measures. We show that under strong consistency these families admit a decomposition into a conditional aggregation function and a univariate conditional risk measure as introduced Hoffmann et al. (2016). Further, in analogy to the univariate cas…

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

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…

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

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

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…

Deep learning solves dynamic programming with recursive utility.

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

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…

Introduces new performance measures using scaled utility functions.

Study scaling limits for option pricing in trinomial models.

CEFOL uses deep learning for dynamic programming with recursive utility.

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 …

New multivariate risk measures improve on univariate OCE methods.

Study risk-sensitive reinforcement learning with optimized certainty equivalents.

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

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…

The future value of a security is described as a random variable. Distribution of this random variable is the formal image of risk uncertainty. On the other side, any present value is defined as a value equivalent to the given future value. This equivalence relationship is a subjective. Thus follows, that present value…

The paper provides guarantees for feedback control with sensor errors.

This paper shows how forward rate interpolations are equivalent to discount factor interpolations in yield curve construction.

The AAA credit rating may have been overly precise given available data.

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

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

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…

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

Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying one specific ensemble method. However, very few works have explored complex fusion schemes using het-erogeneous ensembles with new aggregatio…

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…

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…

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 …

Investment strategy in uncertain markets improved by learning and risk-ambiguity preferences.

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

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

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…

New method for dynamic valuation in markets with random endowments.

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

Scores measure certainty and doubt in classification predictions.

The paper solves portfolio selection using Rényi divergence and optimization.

A new formula reveals symmetries between mean excess and ES functions.

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