Intensity‐based estimation of extreme loss event probability and value at riskHamidieh, Kamal; Stoev, Stilian; Michailidis, George
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1915
We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self‐exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.
A Markov Chain Monte Carlo comparison of variance estimators for the sampling of particulate mixturesCheng, Hao; Geelhoed, Bastiaan; Bode, Peter
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.878
During the sampling of particulate mixtures, samples taken are analyzed for their mass concentration, which generally has non‐zero sample‐to‐sample variance. Bias, variance, and mean squared error (MSE) of a number of variance estimators, derived by Geelhoed, were studied in this article. The Monte Carlo simulation was applied using an observable first‐order Markov Chain with transition probabilities that served as a model for the sample drawing process. Because the bias and variance of a variance estimator could depend on the specific circumstances under which it is applied, Monte Carlo simulation was performed for a wide range of practically relevant scenarios. Using the ‘smallest mean squared error’ as a criterion, an adaptation of an estimator based on a first‐order Taylor linearization of the sample concentration is the best. An estimator based on the Horvitz–Thompson estimator is not practically applicable because of the potentially high MSE for the cases studied. The results indicate that the Poisson estimator leads to a biased estimator for the variance of fundamental sampling error (up to 428% absolute value of relative bias) in case of low levels of grouping and segregation. The uncertainty of the results obtained by the simulations was also addressed and it was found that the results were not significantly affected. The potentials of a recently described other approach are discussed for extending the first‐order Markov Chain described here to account also for higher levels of grouping and segregation. Copyright © 2013 John Wiley & Sons, Ltd.
Analysing markets within the latent class approach: an application to the pharma sectorBassi, Francesca
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1910
In this paper, the latent class approach is applied to an analysis of the Italian pharmaceutical market. The application serves as an example of how fruitfully latent class methodology can be implemented in marketing research. The sector in question shows a high level of competitiveness, more limited economic budgets than years ago and, at the same time, expensive sales and promotion activities; in this context, it is very important to know the reference market to design appropriate marketing strategies. Taking into account the hierarchical structure of the data, this paper: (i) identifies groups of doctors with similar attitudes toward pharmaceutical representatives' work and (ii) verifies which aspects of promotional activity are significant to influence prescription quantities. Copyright © 2012 John Wiley & Sons, Ltd.
A multiblock PLS‐based algorithm applied to a causal model in marketingServera‐Francés, David; Arteaga‐Moreno, Francisco; Gil‐Saura, Irene; Gallarza, Martina G.
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1913
A modern approach to logistics allows it to be understood and used for its capacity to generate value, because value is managerially important as a strategic objective for any firm. In the present work a particular view of this approach is offered by providing a structural model where logistics service quality and sacrifices contribute to the formation of logistics value, but where service quality is also an important determinant of satisfaction. This combined approach, tested with multiblock partial least‐sqaures path modelling, in the particular setting of a business‐to‐business encounter, provides empirical support for a chain of effects between service quality–logistics value–satisfaction–loyalty without underestimating the important effect of service quality on satisfaction and satisfaction on loyalty in industrial settings. This proposed conceptual model of the relationship between customer loyalty and the various contributing factors to that loyalty is the main contribution in this paper. Copyright © 2012 John Wiley & Sons, Ltd.
A parsimonious stochastic model for forecasting gamers' revenues in casinosHui, Sam K.
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1914
The gaming industry is the largest entertainment industry in the United States, with more than $80 billion in revenue annually. Because of the stochasticity of gambling outcomes and the complexity of the casino context, forecasting individual‐level revenues in a casino setting is extremely challenging, and yet crucial for customer relationship management. Current approaches for customer base analysis are usually too general to handle the unique context of the casino setting. To fill this gap between research and practice, this paper develops a stochastic model that incorporates visitation, wagering, and gambling outcomes to forecast gamers' revenues for a major casino operator. The proposed model is parsimonious and can be scaled to handle massive casino customer databases. Despite its parsimony, a holdout prediction test shows that the proposed model provides more accurate individual‐level revenue predictions than other forecasting methods that are based only on the observed data. Copyright © 2012 John Wiley & Sons, Ltd.
Parameter estimation for partially observable systems subject to random failureKim, Michael Jong; Makis, Viliam; Jiang, Rui
2013 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.1920
In this paper, we present a parameter estimation procedure for a condition‐based maintenance model under partial observations. Systems can be in a healthy or unhealthy operational state, or in a failure state. System deterioration is driven by a continuous time homogeneous Markov chain and the system state is unobservable, except the failure state. Vector information that is stochastically related to the system state is obtained through condition monitoring at equidistant sampling times. Two types of data histories are available — data histories that end with observable failure, and censored data histories that end when the system has been suspended from operation but has not failed. The state and observation processes are modeled in the hidden Markov framework and the model parameters are estimated using the expectation–maximization algorithm. We show that both the pseudolikelihood function and the parameter updates in each iteration of the expectation–maximization algorithm have explicit formulas. A numerical example is developed using real multivariate spectrometric oil data coming from the failing transmission units of 240‐ton heavy hauler trucks used in the Athabasca oil sands of Alberta, Canada. Copyright © 2012 John Wiley & Sons, Ltd.