Applied Stochastic Models in Business and Industry

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2199

No abstract is available for this article.

Lu, Wanbo; Ke, Rui

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2259

In this paper, we extend the closed form moment estimator (ordinary MCFE) for the autoregressive conditional duration model given by Lu et al (2016) and propose some closed form robust moment‐based estimators for the multiplicative error model to deal with the additive and innovational outliers. The robustification of the closed form estimator is done by replacing the sample mean and sample autocorrelation with some robust estimators. These estimators are more robust than the quasi‐maximum likelihood estimator (QMLE) often used to estimate this model, and they are easy to implement and do not require the use of any numerical optimization procedure and the choice of initial value. The performance of our proposal in estimating the parameters and forecasting conditional mean μt of the MEM(1,1) process is compared with the proposals existing in the literature via Monte Carlo experiments, and the results of these experiments show that our proposal outperforms the ordinary MCFE, QMLE, and least absolute deviation estimator in the presence of outliers in general. Finally, we fit the price durations of IBM stock with the robust closed form estimators and the benchmarks and analyze their performances in estimating model parameters and forecasting the irregularly spaced intraday Value at Risk.

Mulero, Julio; Sordo, Miguel A.; de Souza, Marilia C.; Suárez‐LLorens, Alfonso

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2260

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.

Souza, Sílvio Alves; Duarte, Denise; Mendes, Eduardo M. A. M.

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2262

In this work, a set of sequences of information (time series), under nonstationary regime, with continuous space state, discrete time, and a Markovian dependence, is considered. A new model that expresses the marginal transition density function of one sequence as a linear combination of the marginal transition density functions of all sequences in the set is proposed. The coefficients of this combination are denominated marginal contribution coefficients and represent how much each transition density function contributes to the calculation of a chosen transition density function. The proposed coefficient is a marginal coefficient because it can be computed instantaneously, and it may change from one time to another time since all calculations are performed before stationarity is reached. This clearly differentiates the new coefficient from well‐known measures such as the cross‐correlation and the coherence. The idea behind the model is that if a specific sequence has a high marginal contribution for the transition density function from another sequence, the first may be replaced by the latter without losing much information that means that the knowledge of few densities should be enough to recover the overall behaviour. Simulations, considering 2 chains, are presented so as to check the sensitivity of the proposed model. The methodology is also applied to a real data originated from a wire‐drawing machine whose main function is to decrease the transverse diameter of metal wires. The behaviour of the level of acceleration of each bearing in relation to the other ones is then verified.

Yan, Nina; Liu, Chongqing; Liu, Ye; Sun, Baowen

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2264

We constructed a Stackelberg game in a supply chain finance (SCF) system including a manufacturer, a capital‐constrained retailer, and a bank that provides loans on the basis of the manufacturer's credit guarantee. To emphasize the financial service providers' risks, we assumed that both the bank and the manufacturer are risk‐averse and formulated trade‐off objective functions for both of them as the convex combination of the expected profit and conditional value‐at‐risk. To explore the effects of the risk preferences and decision preferences on SCF equilibriums, we mathematically analyzed the optimal order quantities, wholesale prices, and interest rates under different risk preference scenarios and performed numerical analyses to quantify the effects. We found that incorporating bank credit with a credit guarantee can effectively balance the retailer's financing risk between the bank and the manufacturer through interest rate charging and wholesale pricing. Moreover, SCF equilibriums with risk aversion are highly affected by the degree of both the lender's and guarantor's risk tolerance in regard to the borrower's default probability and will be more conservative than those in the risk‐neutral cases that only maximize expected profit.

Lim, Johan; Lee, Sungim

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2267

The generalized T2 chart (GT‐chart), which is composed of the T2 statistic based on a small number of principal components and the remaining components, is a popular alternative to the traditional Hotelling's T2 control chart. However, the application of the GT‐chart to high‐dimensional data, which are now ubiquitous, encounters difficulties from high dimensionality similar to other multivariate procedures. The sample principal components and their eigenvalues do not consistently estimate the population values, and the GT‐chart relying on them is also inconsistent in estimating the control limits. In this paper, we investigate the effects of high dimensionality on the GT‐chart and then propose a corrected GT‐chart using the recent results of random matrix theory for the spiked covariance model. We numerically show that the corrected GT‐chart exhibits superior performance compared to the existing methods, including the GT‐chart and Hotelling's T2 control chart, under various high‐dimensional cases. Finally, we apply the proposed corrected GT‐chart to monitor chemical processes introduced in the literature.

Shakibayifar, Masoud; Hassannayebi, Erfan; Jafary, Hossein; Sajedinejad, Arman

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2268

Urban rail planning is extremely complex, mainly because it is a decision problem under different uncertainties. In practice, travel demand is generally uncertain, and therefore, the timetabling decisions must be based on accurate estimation. This research addresses the optimization of train timetable at public transit terminals of an urban rail in a stochastic setting. To cope with stochastic fluctuation of arrival rates, a two‐stage stochastic programming model is developed. The objective is to construct a daily train schedule that minimizes the expected waiting time of passengers. Due to the high computational cost of evaluating the expected value objective, the sample average approximation method is applied. The method provided statistical estimations of the optimality gap as well as lower and upper bounds and the associated confidence intervals. Numerical experiments are performed to evaluate the performance of the proposed model and the solution method.

Tyssedal, John; Chaudhry, Muhammad Azam

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2269

A screening design is an experimental plan used for identifying the expectedly few active factors from potentially many. In this paper, we compare the performances of 3 experimental plans, a Plackett‐Burman design, a minimum run resolution IV design, and a definitive screening design, all with 12 and 13 runs, when they are used for screening and 3 out of 6 factors are active. The functional relationship between the response and the factors was allowed to be of 2 types, a second‐order model and a model with all main effects and interactions included. D‐efficiencies for the designs ability to estimate parameters in such models were computed, but it turned out that these are not very informative for comparing the screening performances of the 2‐level designs to the definitive screening design. The overall screening performance of the 2‐level designs was quite good, but there exist situations where the definitive screening design, allowing both screening and estimation of second‐order models in the same operation, has a reasonable high probability of being successful.

Lamberti, Giuseppe; Banet Aluja, Tomas; Sanchez, Gaston

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2270

The problem of heterogeneity represents a very important issue in the decision‐making process. Furthermore, it has become common practice in the context of marketing research to assume that different population parameters are possible depending on sociodemographic and psycho‐demographic variables such as age, gender, and social status. In recent decades, numerous approaches have been proposed with the aim of involving heterogeneity in the parameter estimation procedures. In partial least squares path modeling, the common practice consists of achieving a global measurement of the differences arising from heterogeneity. This leaves the analyst with the important task of detecting, a posteriori, which are the causal relationships (ie, path coefficients) that produce changes in the model. This is the case in Pathmox analysis, which solves the heterogeneity problem by building a binary tree to detect those segments of population that cause the heterogeneity. In this article, we propose extending the same Pathmox methodology to asses which particular endogenous equation of the structural model and which path coefficients are responsible of the difference.

Heaton, J. B.; Polson, N. G.; Witte, J. H.

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2271

We develop a simple stock selection model to explain why active equity managers tend to underperform a benchmark index. We motivate our model with the empirical observation that the best performing stocks in a broad market index often perform much better than the other stocks in the index. Randomly selecting a subset of securities from the index may dramatically increase the chance of underperforming the index. The relative likelihood of underperformance by investors choosing active management likely is much more important than the loss those same investors take due to the higher fees of active management relative to passive index investing. Thus, active management may be even more challenging than previously believed, and the stakes for finding the best active managers may be larger than previously assumed.