Stochastic dominance in an ordinal world

Existing stochastic dominance rules apply to variables such as income, wealth and rates of return, all of which are measured on cardinal scales. This study develops and applies stochastic dominance rules for ordinal data. It is shown that the new rules are consistent with the traditional von Neumann-Morgenstern expected utility approach, and that they are applicable and relevant in a wide variety of managerial decision making situations, where existing stochastic dominance rules fail to apply. We apply ordinal SD rules to the transformation of random variables.     

Robust stochastic dominance and its application to risk-averse optimization

We introduce a new preference relation in the space of random variables, which we call robust stochastic dominance. We consider stochastic optimization problems where risk-aversion is expressed by a robust stochastic dominance constraint. These are composite semi-infinite optimization problems with constraints on compositions of measures of risk and utility functions. We develop necessary and sufficient conditions of optimality for such optimization problems in the convex case. In the nonconvex case, we derive necessary conditions of optimality under additional smoothness assumptions of some mappings involved in the problem.     

Fuzzy random renewal process and renewal reward process

So far, there have been several concepts about fuzzy random variables and their expected values in literature. One of the concepts defined by Liu and Liu (2003a) is that the fuzzy random variable is a measurable function from a probability space to a collection of fuzzy variables and its expected value is described as a scalar number. Based on the concepts, this paper addresses two processes—fuzzy random renewal process and fuzzy random renewal reward process. In the fuzzy random renewal process, the interarrival times are characterized as fuzzy random variables and a fuzzy random elementary renewal theorem on the limit value of the expected renewal rate of the process is presented. In the fuzzy random renewal reward process, both the interarrival times and rewards are depicted as fuzzy random variables and a fuzzy random renewal reward theorem on the limit value of the long-run expected reward per unit time is provided. The results obtained in this paper coincide with those in stochastic case or in fuzzy case when the fuzzy random variables degenerate to random variables or to fuzzy variables.     

Stochastic dominance and Friedman-Savage utility functions

Two-way stochastic dominance is defined as the ordering corresponding to the unanimous ranking given by all risk-averse and risk-loving agents. This new ordering of random incomes is shown to have an intuitive economic interpretation and to be a potential substitute for first degree dominance. Two-way dominance is shown to be closely related to a class of utility functions which has been studied by Friedman and Savage. The analysis draws on the geometry of cone orderings, a technique which yields rather directly the price characterization of random variables efficient with respect to two-way dominance.     

Using fuzzy random variables in life annuities pricing

This paper develops life annuity pricing with stochastic representation of mortality and fuzzy quantification of interest rates. We show that modelling the present value of annuities with fuzzy random variables allows quantifying their expected price and risk resulting from the uncertainty sources considered. So, we firstly describe fuzzy random variables and define some associated measures: the mathematical expectation, the variance, distribution function and quantiles. Secondly, we show several ways to estimate the discount rates to price annuities. Subsequently, the present value of life annuities is modelled with fuzzy random variables. We finally show how an actuary can quantify the price and the risk of a portfolio of annuities when their present value is given by means of fuzzy random variables.     

A risk approach by credibility theory

This paper attempts to treat some topics of risk theory by means of credibility theory. We study the risk aversion of an agent faced with a situation of uncertainty represented by a discrete fuzzy variable, the relationship between stochastic dominance and credibilistic dominance, and an index of riskiness of discrete credibilistic gambles. In the framework of an optimal saving credibilistic model, the way the presence of risk modifies the level of optimal saving is studied. The main tool of our investigation is an operator defined by B. Liu and Y. K. Liu by which to a discrete fuzzy variable one associates a discrete random variable with the same expected value as the former.     

Two stochastic dominance criteria based on tail comparisons

Actuarial risks and financial asset returns are typically heavy tailed. In this paper, we introduce 2 stochastic dominance criteria, called the right‐tail order and the left‐tail order, to compare these variables stochastically. The criteria are based on comparisons of expected utilities, for 2 classes of utility functions that give more weight to the right or the left tail (depending on the context) of the distributions. We study their properties, applications, and connections with other classical criteria, including the increasing convex and the second‐order stochastic dominance. Finally, we rank some parametric families of distributions and provide empirical evidence of the new stochastic dominance criteria with an example using real data.     

基于随机参照点的均值-风险分析

本文依据参照依赖偏好模型提出了基于随机参照点的风险度量方法,进而构建了均值-风险模型,并讨论了该决策方法与随机占优之间的一致性。研究发现,该决策方法不仅与一级随机占优是一致的而且与二级随机占优也是一致的。由于二级随机占优与期望效用理论的一致性,因而所构建的均值-风险模型与期望效用理论也是一致的。     

On stochastic management modeling with dependent variables

This paper presents an integrated framework for handling dependent random variables in a large class of stochastic management models, a class that includes stochastic break-even analysis and stochastic present-value analysis. We first demonstrate that the common approach of modeling dependent random variables is usually surprisingly inadequate, and a general “functional approach” is presented as a practical modeling alternative. Adopting this modeling approach, we then present a procedure for deriving the stochastic characteristics of the model's objective variable. In the context of stochastic breakeven analysis, this means determining the probabilities of achieving various profit levels and the expected utility of the stochastic profit. The procedure allows the model's random variables to assume diverse distribution and dependency forms, and the simplicity and reliability of the procedure is demonstrated by a numerical example.     

Characterizations on Almost Stochastic Dominance Revisited

Almost stochastic dominance has been receiving more attention in the financial and economic literature. In this short note, we characterize the almost first- and second-degree stochastic dominance by requiring one distribution to be ``close to' a new distribution that dominates or is dominated by another distribution in the traditional sense of the first- and second-order stochastic dominance, respectively. We also investigate the concept of almost stochastic dominance for unbounded random variables.     

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??Almost stochastic dominance has been receiving more attention in the financial and economic literature. In this short note, we characterize the almost first- and second-degree stochastic dominance by requiring one distribution to be ``close to' a new distribution that dominates or is dominated by another distribution in the traditional sense of the first- and second-order stochastic dominance, respectively. We also investigate the concept of almost stochastic dominance for unbounded random variables.     

Optimization with multivariate stochastic dominance constraints

We consider stochastic optimization problems where risk-aversion is expressed by a stochastic ordering constraint. The constraint requires that a random vector depending on our decisions stochastically dominates a given benchmark random vector. We identify a suitable multivariate stochastic order and describe its generator in terms of von Neumann–Morgenstern utility functions. We develop necessary and sufficient conditions of optimality and duality relations for optimization problems with this constraint. Assuming convexity we show that the Lagrange multipliers corresponding to dominance constraints are elements of the generator of this order, thus refining and generalizing earlier results for optimization under univariate stochastic dominance constraints. Furthermore, we obtain necessary conditions of optimality for non-convex problems under additional smoothness assumptions.     

On consistency of stochastic dominance and mean–semideviation models

We analyze relations between two methods frequently used for modeling the choice among uncertain outcomes: stochastic dominance and mean–risk approaches. New necessary conditions for stochastic dominance are developed. These conditions compare values of a certain functional, which contains two components: the expected value of a random outcome and a risk term represented by the central semideviation of the corresponding degree. If the weight of the semideviation in the composite objective does not exceed the weight of the expected value, maximization of such a functional yields solutions which are efficient in terms of stochastic dominance. The results are illustrated graphically. Received: September 15, 1998 / Accepted: October 1, 2000?Published online December 15, 2000     

Cycle-transitive comparison of independent random variables

The discrete dice model, previously introduced by the present authors, essentially amounts to the pairwise comparison of a collection of independent discrete random variables that are uniformly distributed on finite integer multisets. This pairwise comparison results in a probabilistic relation that exhibits a particular type of transitivity, called dice-transitivity. In this paper, the discrete dice model is generalized with the purpose of pairwisely comparing independent discrete or continuous random variables with arbitrary probability distributions. It is shown that the probabilistic relation generated by a collection of arbitrary independent random variables is still dice-transitive. Interestingly, this probabilistic relation can be seen as a graded alternative to the concept of stochastic dominance. Furthermore, when the marginal distributions of the random variables belong to the same parametric family of distributions, the probabilistic relation exhibits interesting types of isostochastic transitivity, such as multiplicative transitivity. Finally, the probabilistic relation generated by a collection of independent normal random variables is proven to be moderately stochastic transitive.     

Gains from diversification on convex combinations: A majorization and stochastic dominance approach

By incorporating both majorization theory and stochastic dominance theory, this paper presents a general theory and a unifying framework for determining the diversification preferences of risk-averse investors and conditions under which they would unanimously judge a particular asset to be superior. In particular, we develop a theory for comparing the preferences of different convex combinations of assets that characterize a portfolio to give higher expected utility by second-order stochastic dominance. Our findings also provide an additional methodology for determining the second-order stochastic dominance efficient set.     

随机模糊的等比例-最小方差投资组合优化研究

Markowitz的均值-方差模型在投资组合优化中得到了广泛的运用和拓展,其中多数拓展模型仅局限于对随机投资组合或模糊投资组合的研究,而忽略了实际问题同时包含了随机信息和模糊信息两个方面。本文首先定义随机模糊变量的方差用以度量投资组合的风险,提出具有阀值约束的最小方差随机模糊投资组合模型,基于随机模糊理论,将该模型转化为具有线性等式和不等式约束的凸二次规划问题。为了提高上述模型的有效性,本文以投资者期望效用最大化为压缩目标对投资组合权重进行压缩,构建等比例-最小方差混合的随机模糊投资组合模型,并求解该模型的最优解。最后,运用滚动实际数据的方法,比较上述两个模型的夏普比率以验证其有效性。     

Chance-constrained programming with fuzzy stochastic coefficients

We consider fuzzy stochastic programming problems with a crisp objective function and linear constraints whose coefficients are fuzzy random variables, in particular of type L-R. To solve this type of problems, we formulate deterministic counterparts of chance-constrained programming with fuzzy stochastic coefficients, by combining constraints on probability of satisfying constraints, as well as their possibility and necessity. We discuss the possible indices for comparing fuzzy quantities by putting together interval orders and statistical preference. We study the convexity of the set of feasible solutions under various assumptions. We also consider the case where fuzzy intervals are viewed as consonant random intervals. The particular cases of type L-R fuzzy Gaussian and discrete random variables are detailed.     

Numerical simulation based on fuzzy stochastic analysis

In this paper mathematical methods for fuzzy stochastic analysis in engineering applications are presented. Fuzzy stochastic analysis maps uncertain input data in the form of fuzzy random variables onto fuzzy random result variables. The operator of the mapping can be any desired deterministic algorithm, e.g. the dynamic analysis of structures. Two different approaches for processing the fuzzy random input data are discussed. For these purposes two types of fuzzy probability distribution functions for describing fuzzy random variables are introduced. On the basis of these two types of fuzzy probability distribution functions two appropriate algorithms for fuzzy stochastic analysis are developed. Both algorithms are demonstrated and compared by way of an example.     

Some fuzzy stochastic orderings for fuzzy random variables

A new approach to stochastic ordering of fuzzy random variables is investigated in this paper. The traditional definitions of stochastic ordering, hazard rate ordering, and also mean residual life ordering were extended and proposed the unified indexes to ranking fuzzy random variables. Finally, we study the stochastic ordering of fuzzy order statistics by using our proposed approach and established some properties.     

On portfolio optimization using fuzzy decisions

We consider stochastic optimization problems involving stochastic dominance constraints. We develop portfolio optimization model involving stochastic dominance constrains using fuzzy decisions and we concentrate on fuzzy linear programming problems with only fuzzy technological coefficients and aplication/implementation of modified subgradient method to fuzy linear programming problems. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)