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This paper studies the supplier selection and order allocation (SS&OA) problem, where risks include a series of disruption scenarios with uncertain probability of occurrence. It is a challenge for industry decision-makers to balance the average cost and the level of risk under the ambiguity set for probabilities. To address this challenge, a two-stage distributionally robust (DR) Mean-CVaR model is presented for the SS&OA problem. A procedure is developed for constructing the ambiguity set, and Polyhedral and Box ambiguity sets are constructed to characterise the uncertain probabilities. The worst-case Mean-CVaR criterion is employed for the second-stage cost within the ambiguity set to trade off the expected cost and CVaR value. Three measures are incorporated to increase the resilience of the supply chain. The proposed robust model is reformulated into two mixed-integer linear programming models. A real case of the Huawei cell phone manufacturer is used to illustrate the validity of the proposed approach in numerical settings. Experimental results show that the new optimising approach can provide a robust SS&OA solution to immunise against the influence caused by uncertain probabilities. By comparative analyses, some management insights are obtained for industry decision-makers. [ABSTRACT FROM AUTHOR]

Nonlinear dependence on a probability measure has recently been encountered with increasing intensity in stochastic optimization. This type of dependence corresponds to many situations in applications; it can appear in problems static (one-stage), dynamic with finite (multi-stage) or infinite horizon, and single- and multi-objective ones. Moreover, the nonlinear dependence can appear not only in the objective functions but also in the constraint sets. In this paper, we will consider static one-objective problems in which the nonlinear dependence appears in the objective function and may also appear in the constraint sets. In detail, we consider "deterministic" constraint sets, whose dependence on the probability measure is nonlinear, constraint sets determined by second-order stochastic dominance, and sets given by mean-risk problems. The last mentioned instance means that the constraint set corresponds to solutions which guarantee acceptable values of both criteria. To obtain relevant assertions, we employ the stability results given by the Wasserstein metric, based on the L 1 norm. We mainly focus on the case in which a solution has to be obtained on the basis of the data and of investigating a relationship between the original problem and its empirical version. [ABSTRACT FROM AUTHOR]

We consider the problem of finding (Pareto-)optimal allocations of risk among finitely many agents. The associated individual risk measures are law-invariant, but with respect to agent-dependent and potentially heterogeneous reference probability measures. Moreover, we assume that the individual risk assessments are consistent with the respective second-order stochastic dominance relations, but remain agnostic about their convexity. A simple sufficient condition for the existence of Pareto optima is provided. The proof combines local comonotonic improvement with a Dieudonné-type argument, which also establishes a link of the optimal allocation problem to the realm of "collapse to the mean" results. [ABSTRACT FROM AUTHOR]

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement's goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance. [ABSTRACT FROM AUTHOR]

This article extends the socially responsible multiobjective problem to (i) estimating optimal portfolios via reward/risk maximization, (ii) including dependence structure between asset returns using vine copulas, and (iii) incorporating enhanced indexation utilizing cumulative zero-order stochastic dominance (CZϵSD). Applying the multiobjective optimal portfolio (MOOP) approach to a sample of EuroStoxx 50 constituents, the results show that the MOOPs provide investors with the flexibility to incorporate different objectives while investing in optimal portfolios. Including social responsibility results in lower portfolio return and economic performance, but at the same time portfolio risk, expected shortfall of portfolio returns below the benchmark, and turnover are reduced. The copula-based predictive models lead to MOOPs with higher returns and reward/risk ratios. Moreover, optimizing environmental scores leads to less risky MOOPs, while optimizing social scores results in higher average return and better risk-adjusted performance. [ABSTRACT FROM AUTHOR]

A 2022 paper [7] introduced the notion of true positive and negative skewness for continuous random variables via Fréchet -means. In this work, we find novel criteria for true skewness and establish true skewness for the Weibull, Lévy, and skew-normal distributions. Furthermore, some relevant properties of the -means of random variables are established. [ABSTRACT FROM AUTHOR]

Problem definition: In this paper, we study the newsvendor problem under some distributional ambiguity sets and explore their relations. Additionally, we explore the benefits of implementing this robust solution in the feature-based newsvendor problem. Methodology and results: We propose a new type of discrepancy-based ambiguity set, the JW ambiguity set, and analyze it within the framework of first-order stochastic dominance. We show that the distributionally robust optimization (DRO) problem with this ambiguity set admits a closed-form solution for the newsvendor loss. This result also implies that the newsvendor problem under the well-known infinity-Wasserstein ambiguity set and Lévy ball ambiguity set admit closed-form inventory levels as a by-product. In the application of feature-based newsvendor, we adopt general kernel methods to estimate the conditional demand distribution and apply our proposed DRO solutions to account for the estimation error. Managerial implications: The closed-form solutions enable an efficient computation of optimal inventory levels. In addition, we explore the property of optimal robust inventory levels with respect to the nonrobust version via concepts of perceived critical ratio and mean repulsion. The results of numerical experiments and the case study indicate that the proposed model outperforms other state-of-the-art approaches, particularly in environments where demand is influenced by covariates and difficult to estimate. Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001, 24-0500-A0001] and National Natural Science Foundation of China [Grant 72331004]. L. Zhang is partially supported by the National Natural Science Foundation of China [Grants 72171156 and 72231002] and the Hong Kong Research Grants Council [Grant 16212419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0159. [ABSTRACT FROM AUTHOR]

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and linear programming. In an application, we use the prospect spanning framework to evaluate whether well-known anomalies are spanned by standard factors. We find that of the strategies considered, a few of them expand the opportunity set of the prospect type investors and thus have real economic value for them and involve absence of loss aversion. Those are the net stock issue anomaly under the FF-5 model, the momentum and net stock issue anomalies under the M-4 model, and the momentum anomaly under the q model. In-sample and out-of-sample results prove remarkably consistent in identifying genuine anomalies for prospect investors. This paper was accepted by Will Cong, finance. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2023.4953. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ambitious pesticide policies aiming to reduce pesticide applications and risks have been introduced across Europe. Herbicides represent a major part of pesticide use, but the uptake of mechanical or agronomic alternatives remains low. We here explore underlying reasons and provide policy insights for supporting a transformation to herbicide‐free production, accounting for both economic and agronomic drivers. We develop detailed extensions to an existing bio‐economic modeling approach and use stochastic dominance analysis to assess the performance of non‐chemical alternatives to herbicides under different production and market conditions exante. We apply our approach to Swiss wheat production and find that herbicide‐free production is not viable without financial support, and thus requires additional per‐hectare agri‐environmental payments and price markups to be economically viable. We find that currently available support payments in Switzerland are sufficient in achieving economic viability of herbicide‐free production. Moreover, we confirm the relevance of risk and risk preferences and identify a risk‐reducing character of herbicide‐free production systems with support payments. Our analysis provides insights on potential drivers, trade‐offs, decision‐making factors, and policies for a transition to non‐chemical weed control. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: We discuss two novel approaches to inter‐distributional comparisons in the classical two‐sample problem. Our starting point is properly standardised and combined, very popular in several areas of statistics and data analysis, ordinal dominance and receiver characteristic curves, denoted by ODC and ROC, respectively. The proposed new curves are termed the comparison curves. Their estimates, being weighted rank processes on (0,1), form the basis of inference. These weighted processes are intuitive, well‐suited for visual inspection of data at hand and are also useful for constructing some formal inferential procedures. They can be applied to several variants of two‐sample problem. Their use can help improve some existing procedures both in terms of power and the ability to identify the sources of departures from the postulated model. To simplify interpretation of finite sample results, we restrict attention to values of the processes on a finite grid of points. This results in the so‐called bar plots (B‐plots), which readably summarise the information contained in the data. What is more, we show that B‐plots along with adjusted simultaneous acceptance regions provide principled information about where the model departs from the data. This leads to a framework that facilitates identification of regions with locally significant differences. We show an implementation of the considered techniques to a standard stochastic dominance testing problem. Some min‐type statistics are introduced and investigated. A simulation study compares two tests pertinent to the comparison curves to well‐established tests in the literature and demonstrates the strong and competitive performance of the former in many typical situations. Some real data applications illustrate simplicity and practical usefulness of the proposed approaches. A range of other applications of considered weighted processes is briefly discussed too. [ABSTRACT FROM AUTHOR]

Introduction: This article introduces a new family of weighted mixture distributions, referred to as θ-WM. The θ-WM family is generated by combining two distributions weighted by a parameter θ, offering notable flexibility to model a wide range of complex phenomena. A special case study of the θ-weighted mixture distribution of Weibull-Lomax (θ-WMWLx) is included, resulting from the combination of Weibull and Lomax distributions. Methods: The research thoroughly examines the reliability and statistical properties of the θ-WMWLx distribution. Key aspects such as stochastic dominance, survival and hazard functions, mean residual life, and moments are addressed. The maximum likelihood method is used to estimate unknown parameters. Results: The research findings show that the θ-WMWLx distribution provides a superior fit compared to competing distributions. The analyses are validated using three real datasets, demonstrating the effectiveness of the proposed distribution. Discussion: The θ-WMWLx distribution stands out for its ability to model complex phenomena with high precision. Validation with real data confirms that the proposed distribution offers a better fit than existing distributions, highlighting its utility and applicability in various statistical analysis contexts. [ABSTRACT FROM AUTHOR]

The paper analyses optimal spending of an endowment fund. The purpose is to find a spending rule which is optimal for the owners and which secures that the fund will last "forever". This we do by finding closed form solutions of the optimal consumption to wealth ratio. We solve this problem using the life cycle model, where the agent can have preferences represented by expected utility or recursive utility. We apply our results to a sovereign wealth fund, and demonstrate that the optimal spending rate is significantly lower than the fund's expected real rate of return, a rule which is in common use. Employing the latter as the spending rate, implies that the fund's value deteriorates both in probability and in expectation, as time goes. For both kinds of long term convergence we find closed form threshold values. Spending below these values secures a sustainable fund. [ABSTRACT FROM AUTHOR]

In a housing market of Shapley and Scarf, each agent is endowed with one indivisible object and has preferences over all objects. An allocation of the objects is in the (strong) core if there exists no (weakly) blocking coalition. We show that, for strict preferences, the unique strong core allocation "respects improvement"—if an agent's object becomes more desirable for some other agents, then the agent's allotment in the unique strong core allocation weakly improves. We extend this result to weak preferences for both the strong core (conditional on nonemptiness) and the set of competitive allocations (using probabilistic allocations and stochastic dominance). There are no counterparts of the latter two results in the two-sided matching literature. We provide examples to show how our results break down when there is a bound on the length of exchange cycles. Respecting improvements is an important property for applications of the housing markets model, such as kidney exchange: it incentivizes each patient to bring the best possible set of donors to the market. We conduct computer simulations using markets that resemble the pools of kidney exchange programs. We compare the game-theoretical solutions with current techniques (maximum size and maximum weight allocations) in terms of violations of the respecting improvement property. We find that game-theoretical solutions fare much better at respecting improvements even when exchange cycles are bounded, and they do so at a low efficiency cost. As a stepping stone for our simulations, we provide novel integer programming formulations for computing core, competitive, and strong core allocations. Funding: P. Biró gratefully acknowledges financial support from the Hungarian Scientific Research Fund, OTKA [Grant K143858] and the Hungarian Academy of Sciences [Grant LP2021-2]. F. Klijn gratefully acknowledges financial support from AGAUR–Generalitat de Catalunya [Grants 2017-SGR-1359 and 2021-SGR-00416] and the Spanish Agencia Estatal de Investigación [Grants ECO2017-88130-P and PID2020-114251GB-I00] (funded by MCIN/AEI/10.13039/501100011033) and the Severo Ochoa Programme for Centres of Excellence in R&D (Barcelona School of Economics) [Grant CEX2019-000915-S]. Research visits related to this work were financed by COST Action [Grant CA15210 ENCKEP], supported by COST (European Cooperation in Science and Technology), http://www.cost.eu/. [ABSTRACT FROM AUTHOR]

The present research discusses the application of risk management tools and Real Option Analysis (ROA) to assess and quantify managerial flexibility in machine replacement decisions under uncertain conditions. Different management configurations are used for the real options approach: options to execute, options to delay, and options to cancel. This reflects the uncertainty inherent to each stage of planning. Uncertainties such as future demand and life-cycle costs are implemented in the model as probability distributions. Monte Carlo simulation is employed to deal with such uncertainties and to facilitate experimental trials. The net present value is used as a decision criterion to determine the best replacement option under different replacement and real option scenarios. Herein, a case study to evaluate different replacement alternatives was conducted for the garment industry. Results of the stochastic net present value, mean-standard-deviation scatter plot, and stochastic dominance showed that the best option was to rent and then buy a new machine of reduced size but greater technological advancement. Finally, tornado diagrams and perfect control methods were used to analyze uncertain factors in order to improve the model and further minimize uncertainty effects. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to estimating the minimum variance hedge ratio (MVHR) based on the wild bootstrap and evaluates the approach using a spectrum of conservative to aggressive alternative hedging strategies associated with the percentiles of the MVHR's bootstrap distribution. This approach is suggested to be more informative and effective relative to the conventional method of hedging solely based on a single-point estimate. Furthermore, the percentile-based MVHRs are robust to influential outliers, non-normality, and unknown forms of heteroskedasticity. The bootstrap percentile-based hedging strategies' effectiveness is compared with those from the naïve method and the asymmetric DCC-GARCH model for a range of financial assets and commodities. The bootstrap percentile-based hedging technique is identified to outperform its alternatives in terms of hedging effectiveness, downside risk, and return variability, suggesting its superiority to other methods in both the literature and in practice. [ABSTRACT FROM AUTHOR]

The investment horizon plays a crucial role in portfolio selection: For horizons approximately up to a year, one can safely employ the mean–variance (M-V) rule. Moreover, if investment consultants use monthly rates of return to derive the M-V efficient set and the investor horizon is longer but smaller than one year, the economic cost induced by this horizon mismatch is negligible. For longer horizons, the M-V rule deviates substantially from expected utility maximization and the economic cost induced by employing the M-V rule is substantial. For relatively long horizons (say 20 or 30 years), despite the argument that with myopic preference the horizon does not matter, small stocks dominate large stocks by the maximum geometric mean (MGM) rule and, in practice, also by expected utility for all economically relevant preferences, as there is almost first-degree stochastic dominance (AFSD). [ABSTRACT FROM AUTHOR]

Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of earnings, productivity, asset returns, test scores, etc.). Specifically, given a sample from each of two distributions, I propose statistical inference methods to learn about the set of utility functions for which the first distribution has higher expected utility than the second distribution. With high probability, an "inner" confidence set is contained within this true set, while an "outer" confidence set contains the true set. Such confidence sets can be formed by inverting a proposed multiple testing procedure that controls the familywise error rate. Theoretical justification comes from empirical process results, given that very large classes of utility functions are generally Donsker (subject to finite moments). The theory additionally justifies a uniform (over utility functions) confidence band of expected utility differences, as well as tests with a utility-based "restricted stochastic dominance" as either the null or alternative hypothesis. Simulated and empirical examples illustrate the methodology. [ABSTRACT FROM AUTHOR]

True benevolence is, most fundamentally, a desire that the world be better. It is natural and common, however, to frame thinking about benevolence indirectly, in terms of a desire to make a difference to how good the world is. This would be an innocuous shift if desires to make a difference were extensionally equivalent to desires that the world be better. This paper shows that at least on some common ways of making a "desire to make a difference" precise, this extensional equivalence fails. Where it fails, "difference-making preferences" run counter to the ideals of benevolence. In particular, in the context of decision making under uncertainty, coupling a "difference-making" framing in a natural way with risk aversion leads to preferences that violate stochastic dominance, and that lead to a strong form of collective defeat, from the point of view of betterness. Difference-making framings and true benevolence are not strictly mutually inconsistent, but agents seeking to implement true benevolence must take care to avoid the various pitfalls that we outline. [ABSTRACT FROM AUTHOR]

We characterize conditions under which a better consumption distribution implies higher welfare. Specifically, here "better consumption" means first‐order stochastic dominance, and "higher welfare" means higher expected utility for every subpopulation of individuals with the same utility function. Although this implication seems natural, we first provide a counterexample wherein better consumption risk allocation outweighs lower consumption. We then show that higher expected utility results from higher consumption in different settings, including fixed dependence (fixed copula) between consumption and individual risk preferences, or alternatively using the rank invariance assumption from the treatment effects literature. These are discussed in several real‐world examples. [ABSTRACT FROM AUTHOR]

The expectation is an example of a descriptive statistic that is monotone with respect to stochastic dominance, and additive for sums of independent random variables. We provide a complete characterization of such statistics, and explore a number of applications to models of individual and group decision‐making. These include a representation of stationary monotone time preferences, extending the work of Fishburn and Rubinstein (1982) to time lotteries. This extension offers a new perspective on risk attitudes toward time, as well as on the aggregation of multiple discount factors. We also offer a novel class of non‐expected utility preferences over gambles which satisfy invariance to background risk as well as betweenness, but are versatile enough to capture mixed risk attitudes. [ABSTRACT FROM AUTHOR]

The Entscheidungsnavi is an open-source decision support system based on multi-attribute utility theory, that offers various methods for dealing with uncertainties. To model decisions with uncertainties, decision-makers can use two categories: Forecast and Parameter Uncertainties. Forecast Uncertainty is modeled with (combined) influence factors using discrete, user-defined probability distributions or predefined 'worst-median-best' distributions. Parameter Uncertainty allows imprecision for utilities, objective weights, and probability distributions. To analyze these uncertainties, the Entscheidungsnavi offers several methods and tools, like a robustness check, based on (Monte Carlo) simulations and a sensitivity analysis. The objective weight analysis provides insights into the effects of different objective weight combinations. Indicator impacts, tornado diagrams, and risk profiles visualize the impact of uncertainties in a decision under risk. Risk profiles also enable a check for stochastic and simulation dominance. This article presents the complete range of methods for dealing with uncertainties in the Entscheidungsnavi using a hypothetical case study. [ABSTRACT FROM AUTHOR]

Cryptocurrency returns are highly nonnormal, casting doubt on the standard performance metrics. We apply almost stochastic dominance, which does not require any assumption about the return distribution or degree of risk aversion. From 29 long–short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the three‐factor coin model of Liu et al. So we develop a new three‐factor model where momentum is replaced by a mispricing factor based on size and risk‐adjusted momentum, which significantly improves pricing performance. [ABSTRACT FROM AUTHOR]

Summary: Location choices of differently skilled workers are analyzed in previous work on labor mobility, which proposes a model that suggests thicker tails in the skill distributions of large cities. This paper replicates the empirical findings of this work by using quantile regression and density plots as employed in the existing study, while also suggesting an alternative testing method for thick tails in the form of an initial stochastic dominance test. The test reveals clear evidence of a thicker lower tail, but the results are less clear for the upper tail, which raises some questions on how to best handle extreme upper tails of skill distributions. [ABSTRACT FROM AUTHOR]

I examine the design of optimal insurance contracts considering the possibility of government disaster relief payments. This work focuses on the impact of (risky and ambiguous) government disaster relief on the shape of optimal private insurance contracts. I demonstrate that the optimal insurance contract is a straight deductible contract in the case of a fixed probability of government relief. This result is robust to ambiguity in the probability of relief payments, even for ambiguity‐averse decision makers. If government disaster relief becomes more likely for larger losses, then the optimal insurance contract features coinsurance above a deductible. I also extend this analysis to more general stochastic dominance relationships between disaster relief and loss magnitude. [ABSTRACT FROM AUTHOR]

This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents a comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. This formulation allows us to identify solutions with finite sample guarantees and solutions that are asymptotically consistent when observations are independent and identically distributed. It is, furthermore, shown to be axiomatically motivated in an environment with distribution ambiguity. Leveraging recent results in the field of robust optimization, we further formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem, both of which can reduce to linear programs under mild conditions. We then propose, to the best of our knowledge, the first exact optimization algorithm for this DRSSDCP, the efficiency of which is confirmed by our numerical results. Finally, we illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach "acceptable" out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Funding: This research was partially supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the Canada Research Chair program [Grant 950-230057], and Groupe d'études et de recherche en analyse des décisions, and it was enabled in part by support provided by Calcul Quebec (https://www.calculquebec.ca/en/) and the Digital Research Alliance of Canada (https://www.alliancecan.ca/). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2387. [ABSTRACT FROM AUTHOR]

We contribute to redefining the criteria based on Almost Stochastic Dominance for better portfolio comparison in four ways. First, we refine the first order of Marginal Conditional Stochastic Dominance (Yitzhaki and Olkin in Concentration indices and concentration curves, Vol 19, Lecture notes-monograph series: stochastic orders and decision under risk, 1991; Shalit and Yitzhaki in Manag Sci 40(5):670–684, 1994), which is designed for pairwise asset comparison. Second, we redefine Almost Marginal Conditional Stochastic Dominance (AMCSD) by Denuit et al. (J Bank Finance 41:57–66, 2014) and Chen et al. (Q Rev Econ Finance 85 (C):260–269, 2022), which considers multiple asset changes in a portfolio, especially in the case of second-order stochastic dominance. Our effort secures the hierarchy property (Guo et al. in Econ Lett 121:252–256, 2013) which is absent in previous studies. Third, we extend the analysis of multiple assets and apply our AMCSD definition and Marginal Conditional Stochastic Dominance. Our AMCSD treatment is confirmed to be more appropriate than those in previous study. Finally, for the sake of portfolio risk management, we compose three hypothetical portfolios with option-based indices for empirical analysis. The empirical outcomes support our efforts. [ABSTRACT FROM AUTHOR]

This study aims to conduct an empirical comparative analysis of the performance of Shari'ah and conventional stock indexes during the period 2017–2023, which includes the COVID-19 pandemic. Additionally, it aims to investigate investors' preferences and analyse the long-term relationship of these indexes, as well as exploring the potential diversification benefits. The research methodology incorporates stochastic dominance analysis, the VARMAX procedure, and Johansen's co-integration approach. The data utilized consists of 31 conventional and 31 Islamic stock indexes, specifically from the FTSE, DJ, MSCI, and S&P series. The results show that there are no long-term co-integration links between 30 out of 31 pairs of Islamic and conventional indexes. While conventional indexes tend to outperform Islamic indexes, they also come with a higher risk. On the other hand, Islamic indexes are considered to be less risky, offering potential diversification opportunities that may be attractive for global portfolios, particularly during periods of financial distress. [ABSTRACT FROM AUTHOR]

We study statistical properties of the optimal value of the sample average approximation (SAA). The focus is on the tail function of the absolute error induced by the SAA, deriving upper estimates of its outcomes dependent on the sample size. The estimates allow to conclude immediately convergence rates for the optimal value of the SAA. As a crucial point, the investigations are based on new types of conditions from the theory of empirical processes which do not rely on pathwise analytical properties of the goal functions. In particular, continuity in the parameter is not imposed in advance as often in the literature on the SAA method. It is also shown that the new condition is satisfied if the paths of the goal functions are H\"older continuous so that the main results carry over in this case. Moreover, the main results are applied to goal functions whose paths are piecewise H\"older continuous as, e.g., in two-stage mixed-integer programs. The main results are shown for classical risk-neutral stochastic programs, but we also demonstrate how to apply them to the sample average approximation of risk-averse stochastic programs. In this respect, we consider stochastic programs expressed in terms of mean upper semideviations and divergence risk measures. [ABSTRACT FROM AUTHOR]

This paper examines the Efficient Market Hypothesis (EMH) in crude oil amid the "financialization of commodity markets" and the "fracking revolution". It applies the generalized spectral derivative test (Hong and Lee 2005) on both West Texas Intermediate and Brent spot and futures markets, alongside a stochastic dominance test (Linton et al., 2005) to investigate arbitrage opportunities across markets and benchmarks. The findings indicate that financialization has made each market more efficient but also created more arbitrage opportunities in spot‐futures markets at both benchmarks. The fracking revolution has fragmented oil markets but had little impact on EMH in individual markets or across markets. [ABSTRACT FROM AUTHOR]

Consider a renewal risk model with stochastic return and stochastic perturbation, where the price process of the investment portfolio is a geometric Lévy process. When the claim sizes have a dependence structure, we derive the asymptotics of the finite-time ruin probability for all subexponential claim sizes. Particularly, when the claim sizes come from a subclass of the subexponential distribution class, the finite-time ruin probability has been estimated for claim sizes with a general dependence structure. [ABSTRACT FROM AUTHOR]

The US Space Force's competitive endurance framework may exacerbate offense-defense balance problems in space. Applying concepts of realism, the security dilemma, and offense-defense balance to the notion of competitive endurance supports a new theory of offense dominance in the space domain. Specifically, advances in military technology, space mobility and logistics, and space domain awareness provide an advantage to attackers and increase the probability of conflict in space. By prioritizing defense-focused technology development, defense-centric doctrine and tactics, and greater information-sharing, the Space Force can offset the factors driving increased advantage to the offense and decrease the likelihood of conflict. [ABSTRACT FROM AUTHOR]

Efficiency plays a crucial role in portfolio optimization. This notion is formulated by means of stochastic optimization techniques. Very often this problem is subject to partial uncertainty or incomplete information on the probability distribution and on the preferences expressed by means of the utility function. In this case both the objective function and the underlying probability measure are not known with precision. To address this kind of issues, we propose to model the notion of incomplete information by means of set-valued analysis and, therefore, we propose two different extensions of the classical model. In the first one we rely on the notion of set-valued function while the second one utilizes the notion of set-valued probability. For both of them we investigate stability properties. These results are also linked to the notion of robustness of the aforementioned problem. Finally we apply the obtained results to portfolio theory and stochastic dominance. [ABSTRACT FROM AUTHOR]

Expected returns, variances, betas, and alphas are all non-linear functions of the investment horizon. This seems to be a fatal conceptual problem for the capital asset pricing model (CAPM), which assumes a unique common horizon for all investors. We show that under the standard assumptions, the theoretical CAPM equilibrium surprisingly holds with the 1-period parameters, even when investors have heterogeneous and possibly much longer horizons. This is true not only for risk-averse investors, but for any investors with non-decreasing preferences, including prospect theory investors. Thus, the widespread practice of using monthly betas to estimate the cost of capital is theoretically justified. [ABSTRACT FROM AUTHOR]

The decision of farmers to reduce fertilizer applications and, thus, the achievement of agri‐environmental policy goals interacts with market price developments. In this study, we analyze how changes in price levels and volatility over time (i.e., 1991–2006 vs. 2007–2022) affected farmers' preferences to reduce fertilizer application using statistical inferences of stochastic dominances. The analysis considers two cropping systems and fertilizer reduction measures: (i) grassland‐based milk production and the use of legumes and (ii) wheat production and the use of variable rate application. We show that the economic value of reducing fertilizer increased over time in both grassland‐based milk and wheat production. However, only in the case of wheat production was the reduction in fertilizer application observed as more risk‐reducing over time. In contrast, in grassland‐based milk production, the co‐movement of fertilizer and milk prices canceled out the increase in risk reduction. We conclude that changes in market price, along with agri‐environmental subsidies, can increasingly incentivize the reduction of fertilizer use. [ABSTRACT FROM AUTHOR]

For most major movies, consumers have a choice to watch them in a theater or on home video. While each viewing channel has its own advantages and disadvantages, consumers are watching the same underlying product—a specific movie. An unanswered question is whether consumers enjoy watching a specific movie more in a theater or video setting. Using IMDb rating data, we find that for most wide-release movies, ratings are higher during the theater window than during the video window. The differences are particularly high for movies with relatively large production budgets and for sequels. [ABSTRACT FROM AUTHOR]

Discontinuities largely influence the mechanical properties of rock joints. However, discontinuity orientation clustering methods often rely on the aggregation and separation of orientation data without full consideration of the prior probability structure of orientation data. This paper proposes a method of optimal clustering by Fisher mixed distribution (OCFMD) for automatic grouping of discontinuity orientation. Based on the Fisher prior probability structure of orientation data, OCFMD can identify optimal group centers and group numbers by balancing the fitting accuracy and dominance of Fisher mixed distributions, and optimal grouping results can be generated by membership calculation. A Newton–Raphson expectation maximization (NR-EM) algorithm is derived for the parameter fitting of Fisher mixed distributions. The Fibonacci sequence is used to generate sample points. In addition, the neighbor probability and density of sample points based on Fisher mixed distributions is derived for fitting accuracy calculation. Several cases of rock slopes and rock tunnel excavation faces are adopted for analyzation. Three clustering algorithms combined with four clustering validity indexes of discontinuity grouping are used for comparison. The results show that OCFMD is more accurate and robust than the other automatic grouping methods in optimal grouping result generation. Highlights: An automatic optimal clustering method of discontinuity orientation is proposed based on Fisher mixed distributions. The balance between fitting accuracy and dominance of Fisher mixed distributions is derived for the selection of optimal grouping results. The grouping results of several traditional clustering algorithms combined with clustering validity indexes are observed to be inconsistent with manual results. The proposed method is more accurate and robust than the compared traditional methods in optimal grouping result generation. The convergence effectiveness, sensitivity of neighbor angle selection and robustness to normal vector variations are validated. [ABSTRACT FROM AUTHOR]

This paper considers a moral hazard problem where the agent can choose any output distribution with a support in a given compact set. The agent's effort‐cost is smooth and increasing in first‐order stochastic dominance. To analyze this model, we develop a generalized notion of the first‐order approach applicable to optimization problems over measures. We demonstrate each output distribution can be implemented and identify those contracts that implement that distribution. These contracts are characterized by a simple first‐order condition for each output that equates the agent's marginal cost of changing the implemented distribution around that output with its marginal benefit. Furthermore, the agent's wage is shown to be increasing in output. Finally, we consider the problem of a profit‐maximizing principal and provide a first‐order characterization of principal‐optimal distributions. [ABSTRACT FROM AUTHOR]

Recent theoretical and empirical advancements highlight the pivotal role played by higher-order moments, such as skewness, in shaping financial decision-making. Nevertheless, contemporary experimental research predominantly relies on limited-outcome lotteries, an oversimplified representation distant from real-world investment dynamics. To bridge this research gap, we conducted a rigorously pre-registered experiment. Our study delves into individuals' preferences for investment opportunities, examining the influence of skewness of continuous probability distributions of returns. We document an inclination towards positively skewed outcome distributions. Furthermore, we uncovered a substitution effect between risk appetite and the sign of skewness. Finally, we unveiled a robust positive correlation between skewness-seeking behavior and a propensity for speculative behavior. Simultaneously, a distinct negative correlation surfaced between skewness-seeking behavior and the perceived risk associated with positive skewness. [ABSTRACT FROM AUTHOR]

Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker's chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method. [ABSTRACT FROM AUTHOR]

To test the relationships of stochastic dominance, this paper firstly proposes two new consistent K-S-type statistics based on a quantile rule. Then, we consider their asymptotic properties. We introduce the bootstrap method to calculate the p-values of our proposed tests and use the Monte Carlo method to compare the power of our proposed test with the DD test and the BD test. The simulations showed that our test performed better than these two tests. Finally, we applied our proposed tests to compare the visibility of four provinces in China and compared the effects with the DD test and BD test. The empirical results showed that the conclusions of our proposed method are more-consistent with the dominance relationships of the four provinces. For a given significance level, the results of our proposed test demonstrate that the visibility of Jiangxi province is the best. The next one is the visibility of Hubei province, which outperforms the visibility of Anhui province. The visibility of Hunan province is the poorest. [ABSTRACT FROM AUTHOR]

This study considers the problem of random assignment of a set of objects to a set of agents based on strict ordinal preferences of the agents. We analyze the fractional Boston rule, which is a generalization of the classical Boston rule to random assignment environments. We introduce two new axioms—stochastic dominance (sd)-rank-efficiency and equal-rank envy-freeness—which are respectively efficiency and fairness conditions that respect preference ranks. Sd-rank-efficiency implies ordinal efficiency, which is an efficiency concept defined by Bogomolnaia and Moulin (J Econ Theory 100(2):295–328, 2001) for random assignment environments in the sense of first-order stochastic dominance. Equal-rank envy-freeness implies equal treatment of equals. Sd-rank-efficiency and equal-rank envy-freeness characterize the fractional Boston rule. [ABSTRACT FROM AUTHOR]

This paper proposed a fusion model of the deep long- and short-term memory network named as deep LSTM and the stochastic dominance named as SD filter method to construct an index-tracking portfolio. We present a practical model that provides investors for portfolio construction using the deep LSTM model on extracting stock features and integrating the dimension deduction ability of the SD approach to the fusion model. Three main conclusions are drawn. First, deep learning in a supervised framework can work in portfolio management with our tuned model. Second, our deep LSTM model with an SD selection filter has enhanced the feature extraction ability to construct an index-tracking portfolio. Third, the linear activator, rectified linear unit, cooperated with SD methods can better reduce the estimation errors than the nonlinear activator in our deep LSTM model. These findings can implement on the portfolio construction in the neural network field. Additionally, entropy suggests to evaluate the learning effect of forecasting. The SD methods also are indicated for prosecuting in other neural network models to extract features of time series data, like transformer-based models. [ABSTRACT FROM AUTHOR]

In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021. [ABSTRACT FROM AUTHOR]

Summary: This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We develop a nonparametric framework to estimate the bounds and establish the asymptotic theory that is uniformly valid over the support of treatment effects. An empirical example illustrates the usefulness of the methods. [ABSTRACT FROM AUTHOR]

We formulate an order over constraint sets A⊆Rℓ, called the parallelogram order, which guarantees that argmin{p ⋅ x:x ∈ A} increases in the product order as A increases in the parallelogram order, for any vector p∈Rℓ. Using this result, we characterize the utility/production functions that lead to normal demand as well as the closely related class of production functions with marginal costs that increase with factor prices. By generalizing the concept of supermodularity, we also characterize the class of production functions for which factors are complements. In the context of decision‐making under uncertainty, our new set order leads to natural generalizations of first‐order stochastic dominance in multi‐prior models. [ABSTRACT FROM AUTHOR]

Theoretical analysis and empirical study results all show that there are situations in reality where observed data are not random variables. Thus, decision-making criteria based on probability theory are not suitable for people to make decisions. This paper proposes an uncertain dominance based on uncertainty theory to offer an alternative decision-making criterion for such situations. The paper first defines a new criterion of first- and second-order uncertain dominance, then proves some necessary conditions of them based on uncertainty theory. Some sufficient and necessary conditions of the first- and second-order uncertain dominance are given when uncertain variables are all normal or linear uncertain variables. In addition, the paper proves the link between the uncertain dominance criterion and the expected utility criterion and shows that the first-order uncertain dominance is suitable for all people to make decisions and the second-order uncertain dominance is suitable for risk-averse people to make decisions. [ABSTRACT FROM AUTHOR]

We analyze sorting in a frictional labor market when workers and jobs have multidimensional characteristics. We say that matching is positive assortative in dimension (j , k) if workers with higher endowment in skill k are matched to a job distribution with higher values of attribute j in the first-order stochastic dominance sense. Crucial for sorting is a single-crossing property of technology. Sorting is positive between worker-job attributes with strong complementarities but negative in other dimensions. Finally, sorting is based on comparative advantage: workers sort into jobs that suit their skill mix rather than their overall skill level. [ABSTRACT FROM AUTHOR]