Pricing European options based on the fuzzy pattern of Black–Scholes formula

The application of fuzzy sets theory to the Black–Scholes formula is proposed in this paper. Owing to the fluctuation of financial market from time to time, some input parameters in the Black–Scholes formula cannot always be expected in the precise sense. Therefore, it is natural to consider the fuzzy interest rate, fuzzy volatility and fuzzy stock price. The fuzzy pattern of Black–Scholes formula and put–call parity relationship are then proposed in this paper. Under these assumptions, the European option price will turn into a fuzzy number. This makes the financial analyst who can pick any European option price with an acceptable belief degree for the later use. In order to obtain the belief degree, an optimization problem has to be solved.     

Fuzzy pricing of geometric Asian options and its algorithm

Owing to the fluctuations of the financial market, input data in the options pricing formula cannot be expected to be precise. This paper discusses the problem of pricing geometric Asian options under the fuzzy environment. We present the fuzzy price of the geometric Asian option under the assumption that the underlying stock price, the risk-free interest rate and the volatility are all fuzzy numbers. This assumption makes the financial investors to pick any geometric Asian option price with an acceptable belief degree. In order to obtain the belief degree, the interpolation search algorithm has been proposed. Some numerical examples are presented to illustrate the rationality and practicability of the model and the algorithm. Finally, an empirical study is performed based on the real data. The empirical study results indicate that the proposed fuzzy pricing model of geometric Asian option is a useful tool for modeling the imprecise problem in the real world.     

Option valuation model with adaptive fuzzy numbers

In this paper, we consider moment properties for a class of quadratic adaptive fuzzy numbers defined in Dubois and Prade [D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, 1980]. The corresponding moments of Trapezoidal Fuzzy Numbers (Tr.F.N’s) and Triangular Fuzzy Numbers (T.F.N’s) turn out to be special cases of the adaptive fuzzy number [S. Bodjanova, Median value and median interval of a fuzzy number, Information Sciences 172 (2005) 73–89]. A numerical example is presented based on the Black–Scholes option pricing formula with quadratic adaptive fuzzy numbers for the characteristics such as volatility parameter, interest rate and stock price. Our approach hinges on a characterization of imprecision by means of fuzzy set theory.     

Generalization of belief and plausibility functions to fuzzy sets based on the sugeno integral

Uncertainty has been treated in science for several decades. It always exists in real systems. Probability has been traditionally used in modeling uncertainty. Belief and plausibility functions based on the Dempster–Shafer theory (DST) become another method of measuring uncertainty, as they have been widely studied and applied in diverse areas. Conversely, a fuzzy set has been successfully used as the idea of partial memberships of multiple classes for the presentation of unsharp boundaries. It is well used as the representation of human knowledge in complex systems. Nowadays, there exist several generalizations of belief and plausibility functions to fuzzy sets in the literature. In this article, we propose a new generalization of belief and plausibility functions to fuzzy sets based on the Sugeno integral. We then make comparisons of the proposed generalization with some existing methods. The results show the effectiveness of the proposed generalization, especially for being able to catch more information about the change of fuzzy focal elements. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1215–1228, 2007.     

Belief and Plausibility Functions on Intuitionistic Fuzzy Sets

Belief and plausibility functions based on Dempster–Shafer theory have been used to measure uncertainty. They are also widely studied and applied in diverse areas. Numerous studies in the literature have presented various generalizations of belief and plausibility functions to fuzzy sets. However, there are still less generalizations of belief and plausibility functions to intuitionistic fuzzy sets. Because intuitionistic fuzzy sets can present the degrees of both membership and nonmembership with a degree of hesitancy, the knowledge and semantic representation becomes more general and applicable than fuzzy sets. In this paper, we propose a generalization of belief and plausibility functions to intuitionistic fuzzy sets based on fuzzy integral. Some numerical examples show the effectiveness of the proposed generalization. Furthermore, this generalization of belief and plausibility functions to intuitionistic fuzzy sets is able to catch more information about the change of intuitionistic fuzzy focal elements.     

Graded tableaux for Rational Pavelka Logic

In this article, a sound and complete tableau system for Rational Pavelka Logic (RPL) is introduced. Extended formulas are used as the counterpart of the graded formulas. In this calculus, if we want to show that the graded formula (x, r) is tableau provable (in the finite fuzzy theory F, respectively), we develop a tableau for the extended formula [r, x] (for the set of extended formulas {[r, x], [x₁, a₁],…, [x_n, a_n] }, respectively). If this tableau closes we claim that (x, r) is tableau provable (in the fuzzy theory F, respectively). We claim also that x is valid at the degree equal to the l.u.b. that allows the closure of the tableaux. Our tableaux are a first step toward efficient procedures of automated deduction in narrow fuzzy logic with truth constants. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1273–1285, 2005.     

Fuzzy coherence measures

Coherence measures are a tool to compare those fuzzy sets that are sensitive to their own similarity as well as to their fuzzy nature. Within this article we can find three generalizations made about the definition of coherence measures: a first one for any fuzzy set, a second one for any definition about strong negation, and a final one for an extension in those coherence measures that, as a result, do not cause a value in the unit interval, but a fuzzy set in that interval. Tools and properties are offered to create coherence measures. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1–11, 2005.     

Entailment for intuitionistic fuzzy sets based on generalized belief structures

Entailment for measure-based belief structures can extend the possible probability value range of variables on a space and obtain more information from variables. However, if the variable space comes from intuitionistic fuzzy sets, the classical entailment for measure-based belief structures will not work in this issue. To deal with this situation, we propose the entailment for intuitionistic fuzzy sets based on generalized belief structures in this paper to apply the entailment for measure based belief structures on space, which is made up of non-membership degree, membership degree and hesitancy degree of a given intuitionistic fuzzy sets. Numerical examples are mentioned to prove the effectively and flexibility of this proposed entailment model. The experimental results indicate that the proposed algorithm can extend the possible probability value range of variables of space efficiently and obtain more information from intuitionistic fuzzy sets.     

An inverse European option problem in estimating the time-dependent volatility function with statistical analysis

An inverse algorithm with the Levenberg–Marquardt method is developed to estimate the time-dependent volatility function that was used in the model of financial analysis for European options from the observed value of option price. Numerical experiments for the inverse algorithm are performed to show the validity of the present study. Moreover, the statistical analysis is also considered here to determine the standard deviation and 99% confidence bounds for the estimated volatility function. Results show that the standard deviations of the estimated volatility function are decreased as the time approaches the expiry date. This implies that a more reliable volatility function can be obtained as time approaches the expiry date.     

Inclusion measures,similarity measures,and the fuzziness of fuzzy sets and their relations

The inclusion measure, the similarity measure, and the fuzziness of fuzzy sets are three important measures in fuzzy set theory. In this article, we investigate the relations among inclusion measures, similarity measures, and the fuzziness of fuzzy sets, prove eight theorems that inclusion measures, similarity measures, and the fuzziness of fuzzy sets can be transformed by each other based on their axiomatic definitions, and propose some new formulas to calculate inclusion measures, similarity measures, and the fuzziness of fuzzy sets. These results can be applied in many fields, such as pattern recognition, image processing, fuzzy neural networks, fuzzy reasoning, and fuzzy control. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 639–653, 2006.     

Structure and stability analysis of general Mamdani fuzzy dynamic models

Mamdani fuzzy models have always been used as black‐box models. Their structures in relation to the conventional model structures are unknown. Moreover, there exist no theoretical methods for rigorously judging model stability and validity. I attempt to provide solutions to these issues for a general class of fuzzy models. They use arbitrary continuous input fuzzy sets, arbitrary fuzzy rules, arbitrary inference methods, Zadeh or product fuzzy logic AND operator, singleton output fuzzy sets, and the centroid defuzzifier. I first show that the fuzzy models belong to the NARX (nonlinear autoregressive with the extra input) model structure, which is one of the most important and widely used structures in classical modeling. I then divide the NARX model structure into three nonlinear types and investigate how the settings of the fuzzy model components, especially input fuzzy sets, dictate the relations between the fuzzy models and these types. I have found that the fuzzy models become type‐2 models if and only if the input fuzzy sets are linear or piecewise linear (e.g., trapezoidal or triangular), becoming type 3 if and only if at least one input fuzzy set is nonlinear. I have also developed an algorithm to transfer type‐2 fuzzy models into type‐1 models as far as their input–output relationships are concerned, which have some important properties not shared by the type‐2 models. Furthermore, a necessary and sufficient condition has been derived for a part of the general fuzzy models to be linear ARX models. I have established a necessary and sufficient condition for judging local stability of type‐1 and type‐2 fuzzy models. It can be used for model validation and control system design. Three numeric examples are provided. Our new findings provide a theoretical foundation for Mamdani fuzzy modeling and make it more consistent with the conventional modeling theory. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 103–125, 2005.     

Fuzzy pricing of American options on stocks with known dividends and its algorithm

The path‐dependent property of American options leads to the complexity of its pricing. Based on the analysis of American options' characteristics and the influence of the stock dividend, the American call option fuzzy pricing method is discussed in this paper. Under the assumption that the price of stock, discount rate, the volatility, and interest rate are all fuzzy numbers, the fuzzy pricing formula of American option is proposed by using the Black–Scholes pricing model. Then the interpolation search algorithm is designed to solve the proposed pricing model. Finally, the validity and accuracy of this model and its algorithm have to be tested with some numerical examples. © 2010 Wiley Periodicals, Inc.     

Answers to queries concerning uncertain and imprecise information in fuzzy relational databases

A model of an extended fuzzy relational database was proposed to accommodate uncertain and imprecise information. We use two supplementary measurements, satisfactory degree and extra degree, for determining the quality of answers to Select‐Project‐Join (SPJ) queries. The method of measurement determines how much satisfactory information is provided and how much truth information is required for a query. The answers to the query thus contain sure answers and maybe answers. The core of this study is the detailed discussion on the quality of answers in an extended fuzzy relation to query processing. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 647–668, 2005.     

An approach to measure the robustness of fuzzy reasoning

Fuzzy reasoning is intensively used in intelligent systems including fuzzy control, classification, expert systems, and networks to name a few dominant categories of such architectures. As being a fundamental construct permeating so many diverse areas, fuzzy reasoning was studied with respect to its fundamental properties such as robustness. The notion of robustness or sensitivity becomes of paramount importance by leading to a more comprehensive understanding of the way in which reasoning processes are developed. In this study, we introduce and study properties of some measures of robustness (or sensitivity) of fuzzy connectives and implication operators and discuss their relationships with perturbation properties of fuzzy sets. The results produced here are compared and contrasted with the previous findings available in the literature. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 393–413, 2005.     

Correlation coefficient for type‐2 fuzzy sets

Type‐2 fuzzy sets are a generalization of the ordinary fuzzy sets in which each fuzzy set is characterized by a fuzzy membership function. In this article we consider how to define the correlation coefficient between two type‐2 fuzzy sets. We have adopted the embedded function model and interpret each type‐2 fuzzy set as a weighted ensemble of ordinary fuzzy sets. Using this interpretation enables us to define a correlation coefficient between two type‐2 fuzzy sets. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 143–153, 2006.     

A neuro‐fuzzy computing technique for modeling laser‐diode nonlinearity in a radio‐over‐fibre link

Neuro‐fuzzy (NF) models are well‐known robust learning systems that combine the advantages of fuzzy sets and neurocomputation theory. This article presents the application of the NF approach to the computation of 3^rd‐order intermodulation power levels in microwave‐intensity‐modulated GaAlAs laser diodes for radio‐over‐fibre communications. The excellent agreement between the computed results and experimental measurements validates the presented NF approach. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005     

A Spectral Element Approximation to Price European Options with One Asset and Stochastic Volatility

We develop a Legendre quadrilateral spectral element approximation for the Black-Scholes equation to price European options with one underlying asset and stochastic volatility. A weak formulation of the equations imposes the boundary conditions naturally along the boundaries where the equation becomes singular, and in particular, we use an energy method to derive boundary conditions at outer boundaries for which the problem is well-posed on a finite domain. Using Heston’s analytical solution as a benchmark, we show that the spectral element approximation along with the proposed boundary conditions gives exponential convergence in the solution and the Greeks to the level of time and boundary errors in a domain of financial interest.     

The collocating local volatility framework – a fresh look at efficient pricing with smile

ABSTRACT

It is a market practice to price exotic derivatives, like callable basket options, with the local volatility model [B. Dupire, Pricing with a smile, Risk 7 (1994), pp. 18–20; E. Derman and I. Kani, Stochastic implied trees: Arbitrage pricing with stochastic term and strike structure of volatility, Int. J. Theor. Appl. Finance 1 (1998), pp. 61–110.] which can, contrary to stochastic volatility frameworks, handle multi-dimensionality easily. On the other hand, a well-known limitation of the nonparametric local volatility model is the necessity of a short-stepping simulation, which, in high dimensions, is computationally expensive. In this article, we propose a new local volatility framework called the collocating local volatility (CLV) model which allows for large Monte Carlo steps and therefore it is computationally efficient. The CLV model is by its construction guaranteed to be almost perfectly calibrated to implied volatility smiles/skews at a given set of expiries. Additionally, the framework allows to control forward volatilities without affecting the fit to plain vanillas. The model requires only a fraction of a second for complete calibration to simple vanilla products.     

On the position of intuitionistic fuzzy set theory in the framework of theories modelling imprecision

Intuitionistic fuzzy sets [K.T. Atanassov, Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia (deposed in Central Science-Technical Library of Bulgarian Academy of Science, 1697/84), 1983 (in Bulgarian)] are an extension of fuzzy set theory in which not only a membership degree is given, but also a non-membership degree, which is more or less independent. Considering the increasing interest in intuitionistic fuzzy sets, it is useful to determine the position of intuitionistic fuzzy set theory in the framework of the different theories modelling imprecision. In this paper we discuss the mathematical relationship between intuitionistic fuzzy sets and other models of imprecision.     

A Note on “A Unified Approach to Similarity Measures Between Intuitionistic Fuzzy Sets”

Chachi and Taheri (Int J Intell Syst 2013;28:669–685) introduced two general classes of similarity measures between intuitionistic fuzzy sets. Also, they claim that the proposed similarity measures perform well in pattern recognition, fuzzy clustering, and decision making. In this note, we address some limitations associated with the proposed similarity measures.