Interval LU-fuzzy arithmetic in the Black and Scholes option pricing

In financial markets people have to cope with a lot of uncertainty while making decisions. Many models have been introduced in the last years to handle vagueness but it is very difficult to capture together all the fundamental characteristics of real markets. Fuzzy modeling for finance seems to have some challenging features describing the financial markets behavior; in this paper we show that the vagueness induced by the fuzzy mathematics can be relevant in modelling objects in finance, especially when a flexible parametrization is adopted to represent the fuzzy numbers. Fuzzy calculus for financial applications requires a big amount of computations and the LU-fuzzy representation produces good results due to the fact that it is computationally fast and it reproduces the essential quality of the shape of fuzzy numbers involved in computations. The paper considers the Black and Scholes option pricing formula, as long as many other have done in the last few years. We suggest the use of the LU-fuzzy parametric representation for fuzzy numbers, introduced in Guerra and Stefanini and improved in Stefanini, Sorini and Guerra, in the framework of the Black and Scholes model for option pricing, everywhere recognized as a benchmark; the details of the computations by the interval fuzzy arithmetic approach and an illustrative example are also incuded.Fuzzy Operations, Option Pricing, Black and Scholes

Low rank surrogates for polymorphic fields with application to fuzzy-stochastic partial differential equations

We consider a general form of fuzzy-stochastic PDEs depending on the interaction of probabilistic and non-probabilistic ("possibilistic") influences. Such a combined modelling of aleatoric and epistemic uncertainties for instance can be applied beneficially in an engineering context for real-world applications, where probabilistic modelling and expert knowledge has to be accounted for. We examine existence and well-definedness of polymorphic PDEs in appropriate function spaces. The fuzzy-stochastic dependence is described in a high-dimensional parameter space, thus easily leading to an exponential complexity in practical computations. To aleviate this severe obstacle in practise, a compressed low-rank approximation of the problem formulation and the solution is derived. This is based on the Hierarchical Tucker format which is constructed with solution samples by a non-intrusive tensor reconstruction algorithm. The performance of the proposed model order reduction approach is demonstrated with two examples. One of these is the ubiquitous groundwater flow model with Karhunen-Loeve coefficient field which is generalized by a fuzzy correlation length

Assessment and Linear Programming under Fuzzy Conditions

A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers for evaluating a group's mean performance, when qualitative grades instead of numerical scores are used for assessing its members' individual performance. Also, a new technique is developed for solving Linear Programming problems with fuzzy coefficients and everyday life applications are presented to illustrate our results.Comment: 19 pages, 3 figure

Fuzzy Set Ranking Methods and Multiple Expert Decision Making

The present report further investigates the multi-criteria decision making tool named Fuzzy Compromise Programming. Comparison of different fuzzy set ranking methods (required for processing fuzzy information) is performed. A complete sensitivity analysis concerning decision maker’s risk preferences was carried out for three water resources systems, and compromise solutions identified. Then, a weights sensitivity analysis was performed on one of the three systems to see whether the rankings would change in response to changing weights. It was found that this particular system was robust to the changes in weights. An inquiry was made into the possibility of modifying Fuzzy Compromise Programming to include participation of multiple decision makers or experts. This was accomplished by merging a technique known as Group Decision Making Under Fuzziness, with Fuzzy Compromise Programming. Modified technique provides support for the group decision making under multiple criteria in a fuzzy environment.https://ir.lib.uwo.ca/wrrr/1001/thumbnail.jp

Low rank surrogates for polymorphic fields with application to fuzzy-stochastic partial differential equations

We consider a general form of fuzzy-stochastic PDEs depending on the interaction of probabilistic and non-probabilistic ("possibilistic") influences. Such a combined modelling of aleatoric and epistemic uncertainties for instance can be applied beneficially in an engineering context for real-world applications, where probabilistic modelling and expert knowledge has to be accounted for. We examine existence and well-definedness of polymorphic PDEs in appropriate function spaces. The fuzzy-stochastic dependence is described in a high-dimensional parameter space, thus easily leading to an exponential complexity in practical computations. To aleviate this severe obstacle in practise, a compressed low-rank approximation of the problem formulation and the solution is derived. This is based on the Hierarchical Tucker format which is constructed with solution samples by a non-intrusive tensor reconstruction algorithm. The performance of the proposed model order reduction approach is demonstrated with two examples. One of these is the ubiquitous groundwater flow model with Karhunen-Loeve coefficient field which is generalized by a fuzzy correlation length

Fuzzy-stochastic FEM-based homogenization framework for materials with polymorphic uncertainties in the microstructure

Uncertainties in the macroscopic response of heterogeneous materials result from two sources: the natural variability in the microstructure's geometry and the lack of sufficient knowledge regarding the microstructure. The first type of uncertainty is denoted aleatoric uncertainty and may be characterized by a known probability density function. The second type of uncertainty is denoted epistemic uncertainty. This kind of uncertainty cannot be described using probabilistic methods. Models considering both sources of uncertainties are called polymorphic. In the case of polymorphic uncertainties, some combination of stochastic methods and fuzzy arithmetic should be used. Thus, in the current work, we examine a fuzzy‐stochastic finite element method–based homogenization framework for materials with random inclusion sizes. We analyze an experimental radii distribution of inclusions and develop a stochastic representative volume element. The stochastic finite element method is used to obtain the material response in the case of random inclusion radii. Due to unavoidable noise in experimental data, an insufficient number of samples, and limited accuracy of the fitting procedure, the radii distribution density cannot be obtained exactly; thus, it is described in terms of fuzzy location and scale parameters. The influence of fuzzy input on the homogenized stress measures is analyzed

One-dimensional Fuzzy Poverty Measure from an Bootstrap Method Perspective

This paper is a contribution to the analysis of deprivation seen as a one-dimensional condition. A most useful tool for such analysis is to view deprivation as a matter of degree, giving a quantitative expression to its intensity for individuals. Such ‘fuzzy’ conceptualisation has been increasingly utilised in  poverty and deprivation research. This paper aims to further develop and refine this strand of research. The concern of the paper is primarily methodological rather than detailed numerical analysis from particular applications. We re-examine the two additional aspects introduced by the use of fuzzy (as distinct from the conventional poor/non-poor dichotomous) measures, namely: the choice of membership functions and the choice of rules for the manipulation of the resulting fuzzy sets, rules defining their intersection and averaging. The relationship of the proposed fuzzy monetary measure with the membership function and an estimate, by confidence interval, of the poverty line

NEW TECHINQE FOR SOLVIND FINITE LEVEL FUZZY NON-LINEAR INTEGRAL EQUATION

In this paper, non linear  finite fuzzy Volterra integral equation of the second kind is considered. The successive approximate method  will be used t o solve it, and comparing with the exact solution and calculate the absolute error between exact and approximate method .  Some numerical examples are prepared to show the efficiency and simplicity of the method