Applied Stochastic Models in Business and Industry

Heyde, C. C.

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.452

Herwartz, Helmut; Reimers, Hans‐Eggert

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.451

We analyse daily changes of two log foreign exchange (FX) rates involving the Deutsche Mark (DEM) for the period 1975–1998, namely FX‐rates measured against the US dollar (USD) and the Japanese yen (JPY). To account for volatility clustering we fit a GARCH(1,1)‐model with leptokurtic innovations. Its parameters are not stable over the sample period and two separate variance regimes are selected for both exchange rate series. The identified points of structural change are close to a change of the monetary policies in the US and Japan, the latter of which is followed by a long period of decreasing asset prices. Having identified subperiods of homogeneous volatility dynamics we concentrate on stylized facts to distinguish these volatility regimes. The bottom level of estimated volatility turns out be considerably higher during the second part of the sample period for both exchange rates. A similar result holds for the average level of volatility and for implied volatility of heavily traded at the money options. Copyright © 2002 John Wiley & Sons, Ltd.

de La Grandville, Olivier; Pakes, Anthony G.; Tricot, Claude

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.448

Expected values and standard deviations of the geometric means of independent positive random variables are useful indicators of the long‐term profitability of an investment, or survival of a biological population. Often these quantities cannot be evaluated in a closed form, or even if they can, there may be a choice between several probability models for the ‘annual’ growth factors. This paper formulates approximations for geometric means and standard deviations. It evaluates their performance and compares the best of them with the exact values for selected probability models of the annual factors. Among these is a new model for annual log‐returns, called the expo‐normal law. This is the law of log X, where X has a normal law conditioned on X>0. Its properties are developed in some detail. It is found that for the ranges of annual means and standard deviations typically encountered in financial applications, the longer horizon values depend little on the choice of probability model, and that, where possible, exact evaluation is computationally simpler than using approximations. Copyright © 2002 John Wiley & Sons, Ltd.

Georgiou, A. C.; Tsantas, N.

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.454

This paper deals with mathematical human resource planning; more specifically, it suggests a new model for a manpower‐planning system. In general, we study a k‐classed hierarchical system where the workforce demand at each time period is satisfied through internal mobility and recruitment. The motivation for this work is based on various European Union incentives, which promote regional or local government assistance programs that could be exploited by firms not only for hiring and training newcomers, but also to improve the skills and knowledge of their existing personnel. In this respect, in our augmented mobility model we establish a new ‘training/standby’ class, which serves as a manpower inventory position for potential recruits. This class, which may very well be internal or external to the system, is incorporated into the framework of a non‐homogeneous Markov chain model. Furthermore, cost objectives are employed using the goal‐programming approach, under different operating assumptions, in order to minimize the operational cost in the presence of system's constraints and regulations. Copyright © 2002 John Wiley & Sons, Ltd.

Mak, T. K.; Nebebe, F.

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.455

We consider in the present paper the analysis of parameter designs in off‐line quality control. The main objective is to seek levels of the production factors that would minimize the expected loss. Unlike classical analyses which focus on the analysis of the mean and variance in minimizing a quadratic loss function, the proposed method is applicable to a general loss function. An appropriate transformation is first sought to eliminate the dependency of the variance on the mean (to achieve ‘separation’ in the terminology of Box). This is accomplished through a preliminary analysis using a recently proposed parametric heteroscedastic regression model. With the dependency of the variance on the mean eliminated, methods with established properties can be applied to estimate simultaneously the mean and the variance functions in the new metric. The expected loss function is then estimated and minimized based on a distributional free procedure using the empirical distribution of the standardized residuals. This alleviates the need for a full parametric model, which, if incorrectly specified, may lead to biased results. Although a transformation is employed as an intermediate step of analysis, the loss function is minimized in its original metric. Copyright © 2002 John Wiley & Sons, Ltd.

Zhang, Xuemei; Jeske, Daniel R.; Pham, Hoang

2002 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.453

Software failures have become the major factor that brings the system down or causes a degradation in the quality of service. For many applications, estimating the software failure rate from a user's perspective helps the development team evaluate the reliability of the software and determine the release time properly. Traditionally, software reliability growth models are applied to system test data with the hope of estimating the software failure rate in the field. Given the aggressive nature by which the software is exercised during system test, as well as unavoidable differences between the test environment and the field environment, the resulting estimate of the failure rate will not typically reflect the user‐perceived failure rate in the field. The goal of this work is to quantify the mismatch between the system test environment and the field environment. A calibration factor is proposed to map the failure rate estimated from the system test data to the failure rate that will be observed in the field. Non‐homogeneous Poisson process models are utilized to estimate the software failure rate in both the system test phase and the field. For projects that have only system test data, use of the calibration factor provides an estimate of the field failure rate that would otherwise be unavailable. For projects that have both system test data and previous field data, the calibration factor can be explicitly evaluated and used to estimate the field failure rate of future releases as their system test data becomes available. Copyright © 2002 John Wiley & Sons, Ltd.