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This paper provides a fuller characterization of the analytical upper bounds for American options than has been available to date. We establish properties required of analytical upper bounds without any direct reliance on the exercise boundary. A class of generalized European claims on the same underlying asset is then proposed as upper bounds. This set contains the existing closed form bounds of Margrabe, (1978) and Chen and Yeh (2002) as special cases and allows randomization of the maturity payoff. Owing to the European nature of the bounds, across-strike arbitrage conditions on option prices seem to carry over to the bounds. Among other things, European option spreads may be viewed as ratio positions on the early exercise option. To tighten the upper bound, we propose a quasi-bound that holds as an upper bound for most situations of interest and seems to offer considerable improvement over the currently available closed form bounds. As an approximation, the discounted value of Chen and Yeh's (2002) bound holds some promise. We also discuss implications for parametric and nonparametric empirical option pricing. Sample option quotes for the European (XEO) and the American (OEX) options on the S&P 100 Index appear well behaved with respect to the upper bound properties but the bid–ask spreads are too wide to permit a synthetic short position in the early exercise option.

Revealed preference axioms provide a simple way of testing data from consumers or firms for consistency with optimizing behavior. The resulting non-parametric tests are very attractive, since they do not require any ad hoc functional form assumptions. A weakness of such tests, however, is that they are non-stochastic. In this paper, we provide a detailed analysis of two non-parametric approaches that can be used to derive statistical tests for utility maximization, which account for random measurement errors in the observed data. These same approaches can also be used to derive tests for separability of the utility function.

Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage.

Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution.

Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time.

Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

Multiple classifier systems have been used widely in computing, communications, and informatics. Combining multiple classifier systems (MCS) has been shown to outperform a single classifier system. It has been demonstrated that improvement in ensemble performance depends on either the diversity among or the performance of individual systems. A variety of diversity measures and ensemble methods have been proposed and studied. However, it remains a challenging problem to estimate the ensemble performance in terms of the performance of and the diversity among individual systems. The purpose of this paper is to study the general problem of estimating ensemble performance for various combination methods using the concept of a performance distribution pattern (PDP).

In particular, the paper establishes upper and lower bounds for majority voting ensemble performance with disagreement diversity measure Dis, weighted majority voting performance in terms of weighted average performance and weighted disagreement diversity, and plurality voting ensemble performance with entropy diversity measure D.

Bounds for these three cases are shown to be tight using the PDP for the input set.

As a consequence of the authors' previous results on diversity equivalence, the results of majority voting ensemble performance can be extended to several other diversity measures. Moreover, the paper showed in the case of majority voting ensemble performance that when the average of individual systems performance P is big enough, the ensemble performance P^m resulting from a maximum (information‐theoretic) entropy PDP is an increasing function with respect to the disagreement diversity Dis. Eight experiments using data sets from various application domains are conducted to demonstrate the complexity, richness, and diverseness of the problem in estimating the ensemble performance.

The purpose of this paper is to improve the information quality of bankruptcy prediction models proposed in the literature by building prediction intervals around the point estimates generated by these models and to determine if the use of the prediction intervals in conjunction with the point estimated yields an improvement in predictive accuracy over traditional models. The authors calculated the point estimates and prediction intervals for a sample of firms from 1991 to 2008. The point estimates and prediction intervals were used in concert to classify firms as bankrupt or non-bankrupt. The accuracy of the tested technique was compared to that of a traditional bankruptcy prediction model. The results indicate that the use of upper and lower bounds in concert with the point estimates yield an improvement in the predictive ability of bankruptcy prediction models. The improvements in overall prediction accuracy and non-bankrupt firm prediction accuracy are statistically significant at the 0.01 level. The authors present a technique that (1) provides a more complete picture of the firm’s status, (2) is derived from multiple forms of evidence, (3) uses a predictive interval technique that is easily repeated, (4) can be generated in a timely manner, (5) can be applied to other bankruptcy prediction models in the literature, and (6) is statistically significantly more accurate than traditional point estimate techniques. The current research is the first known study to use the combination of point estimates and prediction intervals to in bankruptcy prediction.

The purpose of this paper is to develop a mathematical solution to estimate the deformation pattern and required power in cold plate rolling using coupled stream function method and upper bound theorem.

In the first place, an admissible velocity field and the geometry of deformation zone are derived from a new stream function. Then, the optimum velocity field is obtained by minimizing the corresponding power function. Also, to calculate the adiabatic heating during high speed rolling operations, a two-dimensional conduction-convection problem is sequentially coupled with the mechanical model. To verify the predictions, rolling experiments on aluminum plates are conducted and also, a finite element analysis is performed by Abaqus/Explicit. The predicted deformation zone is then compared with the experimentally measured region as well as with the results of the finite element analysis.

The results show that the predicted deformation zone and the temperature distribution fit reasonably with the experimental data while much lower computational cost needs comparing to the fully finite element analysis.

A new stream function is proposed to properly describe the velocity field and deformation pattern during plate rolling considering the neutral point. Furthermore, the employed algorithm can be simply coupled with the thermal finite element analysis.

In order to make grey relational analysis applicable to the interval grey number, this paper discusses the model of grey relational degree of the interval grey number and uses it to analyze the related factors of China's technological innovation ability.

First, this paper gives the definitions of the lower bound domain, the value domain, the upper bound domain of interval grey number and the generalized measure and the generalized greyness of interval grey number. Then, based on the grey relational theory, this paper proposes the model of greyness relational degree of the interval grey number and analyzes its relationship with the classical grey relational degree. Finally, the model of greyness relational degree is applied to analyze the related factors of China's technological innovation ability.

The results show that the model of greyness relational degree has strict theoretical basis, convenient calculation and easy programming and can be applied to the grey number sequence, real number sequence and grey number and real number coexisting sequence. The relational order of the four related factors of China's technological innovation ability is research and development (R&D) expenditure, R&D personnel, university student number and public library number, and it is in line with the reality.

The results show that the sequence values of greyness relational degree have large discreteness, and it is feasible and effective to analyze the related factors of China's technological innovation ability.

The paper succeeds in realizing both the model of greyness relational degree of interval grey number with unvalued information distribution and the order of related factors of China's technological innovation ability.

Adopting the circular economy (CE) notion in the supply chain perspective is necessary for the sustainability viewpoint. However, such practices are deficient, especially in developing countries like India, because of several obstacles. The purpose of this study was to create an approach for circular supply chain management (CSCM) adaption in Indian rubber industries by identifying and evaluating its associated obstacles.

A hybrid approach of analytic hierarchy process (AHP) and the grey-based ELECTRE method had been employed in this research to obtain the mutual rankings of the identified obstacles based on their impressions on the CSCM prosperity criteria through a case study and involving diverse expert's opinions.

Presented study's findings illustrate that “Lack of consumer knowledge and consciousness towards environmental sustainability” was found to be the top-ranked obstacle followed by “Unwillingness towards supply chain re-structuring”.

The obstacles' prioritized rankings could help leaders to create sequential strategies for adapting a resilient CSCM structure by systematically eliminating these obstacles. Moreover, the pinpointed critical obstacles could be investigated further in separate studies and generate future studies' scope.

During the extensive literature survey, it had been found that the CSCM practices are in the fledgling stage in the developing country's context. Moreover, studies related to CSCM adaption in rubber-based manufacturing industries were much lacking. Presented work is peculiar, aiming to accelerate the CSCM adaption in the industrial rubber sector in developing countries like India.

This paper aims to investigate the determinants of bilateral exports of India to the USA by taking the non-linearity issue in export demand equations which is neglected so far in the empirical work. The study tries to know the reaction of change in exports to exchange rate changes in a non-liner fashion. For this purpose, non-linear autoregressive distributed lag (NARDL) bounds testing approach of Shin et al. (2011) has been used. This approach allows testing for non-linearities both in the short and long run, which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The overall results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The assumed linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations delivers plausible results. If exchange rate non-linearities are detected, it would seem that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The paper uses the newly developed non-linear ARDL framework of Shin et al. (2011) to investigate whether there are non-linearities with respect to the exchange rate for India’s exports to the USA. One of the important features of this framework is that it is free from unit root pre-testing and can be applied regardless of whether variables are I(0) or I(1). In addition, ARDL and NARDL technique efficiently determines the cointegrating relation in small sample. The short-run and long-run parameters with appropriate asymptotic inferences can be obtained by applying OLS to NARDL with an appropriate lag length. Following is the NARDL representation of equation 4(a) and 4(b). For brevity, this is illustrated for 4(a) only, where is the first difference operator, P is the drift component and it is the white noise residual, the coefficients ?_1 to ?_4 represent the long-run relationship, whereas remaining expressions with summation sign represent the short-term dynamics of the model. This equation nests the linear ARDL model presented in Pesarean et al. (2001) for the case of d_k^+=d_k^-and ?_2=?_3for all k. Thus, equation is less restrictive than a linear model. For this test, as its distribution is non-standard, Pesarean et al. (2001) tabulate the critical values. The bound test is used to examine the existence of the long-run relationship among the variables in the system. This test is based on Wald/F-statistic and follows a non-standard distribution. To check whether a cointegrating relationship exists, one has to test the null hypothesis ?_1=?_2=?_3=?_4 = 0 in the equation. Pesarean et al. (2001) provide two sets of critical values in which lower critical bound assumes that all the variables in the ARDL are I(0) and upper critical bound assumes I(1). The null hypothesis of cointegration is rejected if the calculated F-statistics is greater than the upper bound critical values. If the F-statistics is below than the lower critical bound, then null hypothesis cannot be rejected; this indicates no cointegration among the variables. If it lies within the lower and upper bounds, the result is inconclusive. After examining the cointegration, long-run coefficients are calculated by estimating the model with the appropriate lag orders based on the Schwarz Information Criteria (SIC). Further, the short-run dynamics of the model is also analyzed by using unrestricted error correction model based on the assumption made by Pesarean et al. (2001). Thus, the error correction version of the NARDL model pertaining to the central export equation can be expressed as: 10; 10, where ? is the speed of adjustment parameter, and EC is the residuals that are obtained from the estimated cointegration model of equation 4(a). The EC term is expressed as 10; 10, where are the OLS estimators obtained from the equation (5a). The coefficients of the lagged variables provide the short-run dynamics of the model covering the equilibrium path. The error correction coefficient ( ) is expected to be less than zero, and its significant value implies the cointegration relation among the variables. Finally, various tests such as serial correlation, functional form, normality and heteroskedasticity have been conducted to check the performance of the model.

Many empirical studies have estimated the elasticities of different final export demand components with respect to the exports because of their importance in trade policy formulation. But all the work has accounted only linearity in the exchange rate in export demand equation. Hence, in this paper, we tried to estimate non-linearities in export demand equation. The study fills the gap in the literature by improving on existing literature with the incorporation of the newly developed NARDL approach of Shin et al. (2011). This approach allows testing for non-linearities both in the short- and in the long run which might give indications of strategic pricing and non-linearities in exchange rate. The empirical analysis is carried out for bilateral export demand relationships of India with the USA for the period from January 1993 until December 2013. The bound test shows that there exists cointegration among the variables. Results show that exports are determined in the long run by foreign demand, exchange rates and relative prices. The long-run coefficients have got the expected sign and are of reasonable magnitude and statistically significant. Regarding non-linearities, the results show that assuming linearity in export demand functions might be too restrictive. Thereby, the one threshold model that distinguishes exchange rate effects between appreciations and depreciations deliver plausible results. If exchange rate non-linearities are detected, it seems that exports respond stronger to appreciations than to depreciations. A reason for this might be that firms perform strategic pricing in international trade to gain or maintain market shares.

The originality of this paper lies in the fact that it applies NARDL approach to Indian trade data (export demand) and analyzes the asymmetrical and non-linear impact of exchange rate changes on Indian exports.

A multi-state linear k-within-(r, s)-of-(m, n): F lattice system consists of m×n components arranged in m rows and n columns. The possible states of the system and its components are: 0, 1, 2, …, H. According to k values, the system can be categorized into three special cases: decreasing, increasing and constant. The system reliability of decreasing and constant cases exists. The purpose of this paper is to evaluate the system reliability in increasing case with i.i.d components, where there is no any algorithm for evaluating the system reliability in this case.

The Boole-Bonferroni bounds were applied for evaluating the reliability of many systems. In this paper, the author reformulated the second-order Boole-Bonferroni bounds to be suitable for the evaluation of the multi-state system reliability. And the author applied these bounds for deriving the lower bound and upper bound of increasing multi-state linear k-within-(r, s)-of-(m, n): F lattice system.

An illustrated example of the proposed bounds and many numerical examples are given. The author tested these examples and concluded the cases that make the new bounds are sharper.

In this paper, the author considered an important and complex system, the multi-state linear k-within-(r, s)-of-(m, n): F lattice system; it is a model for many applications, for example, telecommunication, radar detection, oil pipeline, mobile communications, inspection procedures and series of microwave towers systems.

This paper suggests a method for the computation of the bounds of increasing multi-state linear k-within-(r,s)-of-(m,n): F lattice system. Furthermore, the author concluded that the cases that make these bounds are sharper.