arXiv research

New insights into risk aversion for complex decision models.

New PFPPs based on rank-dependent utility for better performance control.

The paper solves an insurance problem using mean-variance and rank-dependent utility theory.

Investigates portfolio selection for rank-dependent utilities in incomplete markets.

New theory extends rank-dependent utility for risk and ambiguity.

Investment strategies for rank-dependent utility agents are derived in a continuous-time market.

Under expected utility the local index of absolute risk aversion has played a central role in many applications. Besides, its link with the "global" concepts of the risk and probability premia has reinforced its attractiveness. This paper shows that, with an appropriate approach, similar developments can be achieved in…

We introduce the concept of forward rank-dependent performance processes, extending the original notion to forward criteria that incorporate probability distortions. A fundamental challenge is how to reconcile the time-consistent nature of forward performance criteria with the time-inconsistency stemming from probabili…

The paper improves transformer generalization bounds using rank-dependent covering number bounds.

This paper investigates the so-called leakage effect of trading strategies generated functionally from rank-dependent portfolio generating functions. This effect measures the loss in wealth of trading strategies due to renewing the portfolio constituent stocks. Theoretically, the leakage effect of a trading strategy is…

Optimal insurance strategy for maximizing RDEU under various premium principles.

New concept of attitude towards probability introduced in risk sharing problems.

Bernard et al. (2015) study an optimal insurance design problem where an individual's preference is of the rank-dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their contracts suffer from a problem of moral hazard for paying more compensation for a…

Study optimal reward schemes for inducing desired player performance in risky contests.

We discuss a natural game of competition and solve the corresponding mean field game with \emph{common noise} when agents' rewards are \emph{rank dependent}. We use this solution to provide an approximate Nash equilibrium for the finite player game and obtain the rate of convergence.

A risk-aware RL approach using RDEU and Wasserstein ball for robust performance.

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete on…

Study asymptotic properties of generalized shortfall risk measures for heavy-tailed risks.

Estimating the strength of dependency between two variables is fundamental for exploratory analysis and many other applications in data mining. For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are depende…

New framework for conditional risk minimization using optimal transport.

Study examines risk premium convergence rates in risk sharing contracts.

In this paper we propose and analyze a class of $N$-player stochastic games that include finite fuel stochastic games as a special case. We first derive sufficient conditions for the Nash equilibrium (NE) in the form of a verification theorem. The associated Quasi-Variational-Inequalities include an essential game comp…

Deep ReLU networks with extra parameters have mostly good loss landscapes.

Study compares LRMC algorithms under dependent sampling in various applications.

Most decision theories, including expected utility theory, rank dependent utility theory and cumulative prospect theory, assume that investors are only interested in the distribution of returns and not in the states of the economy in which income is received. Optimal payoffs have their lowest outcomes when the economy …

Diversification represents the idea of choosing variety over uniformity. Within the theory of choice, desirability of diversification is axiomatized as preference for a convex combination of choices that are equivalently ranked. This corresponds to the notion of risk aversion when one assumes the von-Neumann-Morgenster…

Ranking recommendation algorithms across datasets using Bradley-Terry model

A new framework tightens risk measure confidence bounds.

Investor maximizes utility from an unknown claim using robust optimization.

Formulates a Dueling Bandits problem for eliciting Kemeny rankings.

Optimal rank-adaptive matrix estimation from linear measurements.

This paper investigates Pareto optimal (PO, for short) insurance contracts in a behavioral finance framework, in which the insured evaluates contracts by the rank-dependent utility (RDU) theory and the insurer by the expected value premium principle. The incentive compatibility constraint is taken into account, so the …

Many investment models in discrete or continuous-time settings boil down to maximizing an objective of the quantile function of the decision variable. This quantile optimization problem is known as the quantile formulation of the original investment problem. Under certain monotonicity assumptions, several schemes to so…

The paper improves tensor completion bounds using spectral gap.

Unified approach to time-inconsistent problems with distribution-dependent rewards.

The study shows how probability weighting can lead to betting in a risk-averse economy.

This paper presents a unified approach based on Wasserstein distance to derive concentration bounds for empirical estimates for two broad classes of risk measures defined in the paper. The classes of risk measures introduced include as special cases well known risk measures from the finance literature such as condition…

This paper analyzes ETFs with Taiwan exposure, finding heavy tails and asymmetric volatility.

New method improves efficiency of non-convex matrix reconstruction.