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on Utility Models and Prospect Theory |
| By: | T. Wienand; B. Magdalou; R. Nock; P. Hufe |
| Abstract: | We develop an axiomatic framework to evaluate income distributions from the perspective of an opportunity-egalitarian social planner. Building on a formal link with the literature on decision theory under ambiguity, we characterize a class of opportunity-sensitive social welfare functions based on a two-stage evaluation: the planner first computes the expected utility of income within each social type, where types consist of individuals sharing the same circumstances beyond their control, and then aggregates these type-specific welfare levels through a transformation reflecting aversion to inequality of opportunity. The evaluation is governed by a single parameter. We provide equivalent representations of the social welfare function, including a mean-divergence form that separates an efficiency term from an inequality term, and we establish an opportunity stochastic dominance criterion. Finally, we derive inequality measures that decompose overall inequality into within-group risk and between-group inequality of opportunity, providing a tractable basis for normative welfare analysis. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.26853 |
| By: | Joseph Marshall |
| Abstract: | This paper studies how to estimate an individual's taste for forming a connection with another individual in a network. It compares the difficulty of estimation with and without the assumption that utility is transferable between individuals, and with and without the assumption that regressors are symmetric across individuals in the pair. I show that when pair-specific regressors are symmetric, the sufficient conditions for consistency and asymptotic normality of the maximum likelihood estimator that assumes transferable utility (TU-MLE) are also sufficient for the maximum likelihood estimator that does not assume transferable utility (NTU-MLE). When regressors are asymmetric, I provide sufficient conditions for the consistency and asymptotic normality of the NTU-MLE. I also provide a specification test to assess the validity of the transferable utility assumption. Two applications from different fields of economics demonstrate the value of my results. I find evidence of researchers using the TU-MLE when the transferable utility assumption is violated, and evidence of researchers using NTU-model-based estimators when the validity of the transferable utility assumption cannot be rejected. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25641 |
| By: | Minkey Chang |
| Abstract: | We study whether a risk-sensitive objective from asset-pricing theory -- recursive utility -- improves reinforcement learning for portfolio allocation. The Bellman equation under recursive utility involves a certainty equivalent (CE) of future value that has no closed form under observed returns; we approximate it by $K$-sample Monte Carlo and train actor-critic (PPO, A2C) on the resulting value target and an approximate advantage estimate (AAE) that generalizes the Bellman residual to multi-step with state-dependent weights. This formulation applies only to critic-based algorithms. On 10 chronological train/test splits of South Korean ETF data, the recursive-utility agent improves on the discounted (naive) baseline in Sharpe ratio, max drawdown, and cumulative return. Derivations, world model and metrics, and full result tables are in the appendices. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.22880 |
| By: | M. Abdellaoui (HEC Paris - Ecole des Hautes Etudes Commerciales, CNRS - Centre National de la Recherche Scientifique); B. Hill (HEC Paris - Recherche - Hors Laboratoire - HEC Paris - Ecole des Hautes Etudes Commerciales, CNRS - Centre National de la Recherche Scientifique, GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique); E. Kemel (HEC Paris - Ecole des Hautes Etudes Commerciales, CNRS - Centre National de la Recherche Scientifique); H. Maafi (UP8 - Université Paris 8) |
| Abstract: | We investigate learning in ambiguous situations where subjects bet on a winning event whose probability depends on an unknown proportion of winning chips in an urn. Varying the number of draws prior to choice allows us to "scan" ambiguity attitudes across differing amounts of information. By separately eliciting posterior beliefs in addition to matching probabilities, we disentangle the impact of learning on ambiguity attitude from its impact on beliefs, including divergences from Bayesian update. Both "raw data" and smooth ambiguity model-based analyses show that learning affects ambiguity attitude in the direction of ambiguity neutrality. Moreover, at small sample sizes, the impact of these changes on preferences is comparable to that of the divergence from Bayesian update. |
| Keywords: | Ambiguity attitude indices, Learning, Bayesian updating, Sampling. JEL Codes: D80, D81, D83, Smooth ambiguity preferences, Ambiguity aversion, Ambiguity, Ambiguity Ambiguity aversion Smooth ambiguity preferences Ambiguity attitude indices Learning Bayesian updating Sampling. JEL Codes: D80 D81 D83 |
| Date: | 2025–06–19 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05541433 |
| By: | Shunzhi Pang |
| Abstract: | As insurers increasingly behave like financial intermediaries and actively participate in capital markets, understanding the dependence structure between insurance and financial risks becomes crucial for insurers' operations. This paper studies dynamic equilibrium insurance pricing when insurers face ambiguity about the correlation between insurance and financial risks and optimally choose underwriting and investment strategies under worst-case beliefs. Correlation ambiguity can generate multiple equilibrium regimes. Contrary to conventional intuition, we find ambiguity does not necessarily increase insurance prices nor reduce insurers' utility. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.18969 |
| By: | Yuechen Dai; Richard Watt (University of Canterbury); Kuntal Das (University of Canterbury) |
| Abstract: | Over the past decade, the number of operational satellites in lower-earth orbit (LEO) has experienced exponential growth. This has led to levels of traffic congestion in the LEO environment that are making it increasingly risky. Satellites can collide with each other, and they can also be destroyed or damaged by orbital debris. While satellite insurance for such risks is not new, it does not seem to have grown at the rate one might expect given the increased number of insurable assets, and the increased risks they face. In fact, some insurers that once offered to underwrite orbital satellites now prefer to stay out of that particular market, citing excessive risk. This article uses standard financial economics modelling to explore whether this is indeed optimal. We find that in fact an expected utility maximizing insurer should always dedicate some of their underwriting capacity to orbital satellites. We also carry out a simulation to show that including orbital satellite insurance within an optimally structured insurance portfolio has the effect of enhancing long-run profitability. |
| Keywords: | Satellite insurance; capital allocation; portfolio optimization; insurance underwriting; diversification; CRRA utility; efficient frontier; space risk |
| JEL: | G22 G11 D81 G32 C61 |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cbt:econwp:26/02 |
| By: | Victor H. Aguiar; Nail Kashaev |
| Abstract: | Modern pretrained time-series foundation models can forecast without task-specific training, but they do not fully incorporate economic behavior. We show that teaching them basic economic logic improves how they predict demand using an experimental panel. We fine-tune Amazon Chronos-2, a transformer-based probabilistic time-series model, on synthetic data generated from utility-maximizing agents. We exploit Afriat's theorem, which guarantees that demand satisfies the Generalized Axiom of Revealed Preference (GARP) if and only if it can be generated by maximizing some utility function subject to a budget constraint. GARP is a simple condition to check that allows us to generate time series from a large class of utilities efficiently. The fine-tuned model serves as a rationality-constrained forecasting prior: it learns price-quantity relations from GARP-consistent synthetic histories and then uses those relations to predict the choices of real consumers. We find that fine-tuning on GARP-consistent synthetic data substantially improves prediction relative to zero-shot Chronos-2 at all forecast horizons we study. Our results show that economic theory can be used to generate structured synthetic data that improves foundation-model predictions when the theory implies observable patterns in the data. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.23993 |
| By: | Silvana M. Pesenti; Thai Nguyen |
| Abstract: | We consider the problem of active portfolio management, where an investor seeks the portfolio with maximal expected utility of the difference between the terminal wealth of their strategy and a proportion of the benchmark's, subject to a budget and a deviation constraint from the benchmark portfolio. As the investor aims at outperforming the benchmark, they choose a divergence that asymmetrically penalises gains and losses as well as penalises underperforming the benchmark more than outperforming it. This is achieved by the recently introduced $\alpha$-Bregman-Wasserstein divergence, subsuming the Bregman-Wasserstein and the popular Wasserstein divergence. We prove existence and uniqueness, characterise the optimal portfolio strategy, and give explicit conditions when the divergence constraints and the budget constraints are binding. We conclude with a numerical illustration of the optimal quantile function in a geometric Brownian motion market model. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20580 |
| By: | A. Fernandez-Perez; A.-M. Fuertes; J. Miffre (Audencia Business School) |
| Abstract: | This paper presents a comprehensive analysis of traditional versus selective hedging strategies in commodity futures markets. Traditional hedging aims solely to reduce spot price risk, while selective hedging also seeks to enhance returns by predicting movements in commodity futures prices. We construct selective hedges using a range of forecasting techniques, from simple historical averages to advanced machine learning models, and evaluate their performance based on the expected mean-variance utility of hedge portfolio returns. Out-of-sample results for 24 commodities do not favor selective hedging over traditional hedging, as the former increases risk without delivering additional returns. These findings are robust across various hedge reformulations, expanding estimation windows, and rebalancing frequencies. |
| Keywords: | Commodity futures markets, Expected utility, Selective hedging, Traditional hedging |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05563835 |
| By: | François Le Grand (ESSEC Business School); Xavier Ragot (Sciences Po - Sciences Po, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po) |
| Abstract: | We analyze optimal fiscal policy in a heterogeneous-agent model with capital accumulation and aggregate shocks, where the government uses public debt, a capital tax, and a progressive labor tax to finance public spending. We first study a tractable model and show that the steady-state optimal capital tax can be positive if credit constraints are occasionally binding. However, the existence of such an equilibrium depends on the shape of the utility function. We also characterize the optimal dynamic of public debt after a public spending shock. We confirm these findings by solving for optimal policy in a general heterogeneous-agent model. |
| Keywords: | D31, E44, H21, public debt JEL codes: E21, optimal fiscal policy, Heterogeneous agents, Heterogeneous agents optimal fiscal policy public debt JEL codes: E21 H21 E44 D31 |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05547657 |
| By: | Yun Zhao; Alex S. L. Tse; Harry Zheng |
| Abstract: | We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. The problem is formulated as a sequential optimal stopping problem over entry and exit times under general utility function and price process. We first consider a relaxed version of the problem in which the stopping times are modeled by the jump times of Cox processes driven by bounded, non-randomized intensity controls. Under the exploratory formulation, the agent's randomized control is characterized via the probability measure over the jump intensities, and their objective function is regularized by Shannon's differential entropy. This yields a system of the exploratory HJB equations and Gibbs distributions in closed-form as the optimal policy. Error estimates and convergence of the RL objective to the value function of the original problem are established. Finally, an RL algorithm is designed, and its implementation is showcased in a pairs-trading application. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02035 |
| By: | Aaron L. Bodoh-Creed; Brent R. Hickman; John A. List; Ian Muir; Gregory K. Sun |
| Abstract: | Nonlinear pricing theory predicts that firms can extract surplus by inducing heterogeneous consumers to self-sort across price contract offers that are ex-post optimal for them. We study subscription pricing when the frictionless sorting assumption fails. Using large-scale subscription experiments conducted by Lyft, we document systematic deviations from optimal self-selection: many high-demand consumers decline subscriptions that would have saved them money, while some subscribers fail to break even. We develop a structural model of intensive-margin demand in which consumers may exhibit salience failures, forecast errors about future demand, or impulsivity. We show that subscription uptake can be recast as one-sided noncompliance in a binary-instrument framework, allowing us to leverage LATE methods to identify counterfactual outcome distributions and a novel “uptake function” linking baseline outcomes to compliance behavior. Combining experimental price variation with this identification strategy, we recover utility primitives, demand heterogeneity, and behavioral parameters. Salience failures and forecast errors play quantitatively important roles. Counterfactual analyses show that optimal subscription pricing generates substantial gains relative to linear pricing, but these gains are highly sensitive to consumer deviations from ex-post optimal choice. Implementing nonlinear pricing therefore requires not only optimal contract design for consumer screening, but also coordinated efforts to mitigate behavioral frictions. |
| JEL: | C14 C51 C93 D04 L4 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35003 |
| By: | Tim J. Boonen; Engel John C. Dela Vega; Len Patrick Dominic M. Garces |
| Abstract: | This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness preference. The reserve level of each line is modeled using a diffusion process. The objective is to obtain a robust strategy that maximizes the expected weighted sum of discounted dividends until the first ruin time, while incorporating a penalty term for the distortion between the reference and alternative models in the worst-case scenario. We completely solve this problem and obtain the value function and optimal (equilibrium) strategies in closed form. We show that the optimal dividend-capital injection strategy is a barrier strategy. The optimal proportion of risk ceded to the reinsurer and the deviation of the worst-case model from the reference model are decreasing with respect to the aggregate reserve level. Finally, numerical examples are presented to show the impact of the model parameters and ambiguity aversion on the optimal strategies. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.25350 |
| By: | Easton Huch; Michael Keane |
| Abstract: | Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference approach using a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. Our proposal includes a specialized neural network architecture and accompanying training procedures designed to respect the invariance properties of discrete choice models. We provide group-theoretic foundations for the architecture, including a proof of universal approximation given a minimal set of invariant features. Once trained, the emulator enables rapid likelihood evaluation and gradient computation. We use Sobolev training, augmenting the likelihood loss with a gradient-matching penalty so that the emulator learns both choice probabilities and their derivatives. We show that emulator-based maximum likelihood estimators are consistent and asymptotically normal under mild approximation conditions, and we provide sandwich standard errors that remain valid even with imperfect likelihood approximation. Simulations show significant gains over the GHK simulator in accuracy and speed. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.24705 |
| By: | Jules Arzel; Noureddine Lehdili |
| Abstract: | This paper studies the problem of hedging and pricing a European call option under proportional transaction costs, from two complementary perspectives. We first derive the optimal hedging strategy under CARA utility, following the stochastic control framework of Davis et al. (1993), characterising the no-transaction band via the Hamilton-Jacobi-Bellman Quasi-Variational Inequality (HJBQVI) and the Whalley-Wilmott asymptotic approximation. We then adopt a deep hedging approach, proposing two architectures that build on the No-Transaction Band Network of Imaki et al. (2023): NTBN-Delta, which makes delta-centring explicit, and WW-NTBN, which incorporates the Whalley-Wilmott formula as a structural prior on the bandwidth and replaces the hard clamp with a differentiable soft clamp. Numerical experiments show that WW-NTBN converges faster, matches the stochastic control no-transaction bands more closely, and generalises well across transaction cost regimes. We further apply both frameworks to the bull call spread, documenting the breakdown of price linearity under transaction costs. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.29994 |
| By: | Ruiwu Liu; Yangjian Zhu |
| Abstract: | Electric vehicles (EVs) require substantially longer refueling times than gasoline vehicles, which can generate severe congestion at charging stations when demand concentrates. We propose a two-stage allocation framework for EV charging networks. In Stage 1, a central coordinator determines station-level admission quotas to control worst-station delay using a queue-informed congestion metric. In Stage 2, given these quotas and feasibility constraints (e.g., reachability), the coordinator solves a utility-maximizing capacitated assignment to allocate EVs across stations. To keep Stage~2 tractable while capturing heterogeneous charging needs, we precompute each EV-station pair's optimal charging amount in closed form under a battery-capacity constraint and then solve a transportation/assignment problem. Finally, we introduce a reduced-form participation model to characterize adoption thresholds under network benefits, spillovers, and coordination costs. Numerical experiments illustrate substantial reductions in worst-case congestion with limited impact on average utility, and highlight scaling patterns as the number of stations increases. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.16202 |