The usefulness of Bayesian optimal designs for discrete choice experimentsKessels, Roselinde; Jones, Bradley; Goos, Peter; Vandebroek, Martina
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.906
Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility‐neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Copyright © 2011 John Wiley & Sons, Ltd.
An examination of HMM‐based investment strategies for asset allocationErlwein, Christina; Mamon, Rogemar; Davison, Matt
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.820
We develop and analyse investment strategies relying on hidden Markov model approaches. In particular, we use filtering techniques to aid an investor in his decision to allocate all of his investment fund to either growth or value stocks at a given time. As this allows the investor to switch between growth and value stocks, we call this first strategy a switching investment strategy. This switching strategy is compared with the strategies of purely investing in growth or value stocks by tracking the quarterly terminal wealth of a hypothetical portfolio for each strategy. Using the data sets on Russell 3000 growth index and Russell 3000 value index compiled by Russell Investment Services for the period 1995–2008, we find that the overall risk‐adjusted performance of the switching strategy is better than that of solely investing in either one of the indices. We also consider a second strategy referred to as a mixed investment strategy which enables the investor to allocate an optimal proportion of his investment between growth and value stocks given a level of risk aversion. Numerical demonstrations are provided using the same data sets on Russell 3000 growth and value indices. The switching investment strategy yields the best or second best Sharpe ratio as compared with those obtained from the pure index strategies and mixed strategy in 14 intervals. The performance of the mixed investment strategy under the HMM setting is also compared with that of the classical mean–variance approach. To make the comparison valid, we choose the same level of risk aversion for each set‐up. Our findings show that the mixed investment strategy within the HMM framework gives higher Sharpe ratios in 5 intervals of the time series than that given by the standard mean–variance approach. The calculated weights through time from the strategy incorporating the HMM set‐up are more stable. A simulation analysis further shows a higher performance stability of the HMM strategies compared with the pure strategies and the mean–variance strategy. Copyright © 2009 John Wiley & Sons, Ltd.
A transformed random effects model with applicationsYang, Zhenlin; Huang, Jianhua
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.822
This paper proposes a transformed random effects model for analyzing non‐normal panel data where both the response and (some of) the covariates are subject to transformations for inducing flexible functional form, normality, homoscedasticity, and simple model structure. We develop a maximum likelihood procedure for model estimation and inference, along with a computational device which makes the estimation procedure feasible in cases of large panels. We provide model specification tests that take into account the fact that parameter values for error components cannot be negative. We illustrate the model and methods with two applications: state production and wage distribution. The empirical results strongly favor the new model to the standard ones where either linear or log‐linear functional form is employed. Monte Carlo simulation shows that maximum likelihood inference is quite robust against mild departure from normality. Copyright © 2009 John Wiley & Sons, Ltd.
Modeling stock index returns by means of partial least‐squares methods: An out‐of‐sample analysis for three stock marketsCengiz, Cetin‐Behzet; Herwartz, Helmut
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.826
We analyze the underlying economic forces of the stock markets in Germany, the U.K. and the U.S. Identifying a number of variables evincing return predictability, we follow a partial least‐squares (PLS) approach to combine these observables into a few latent factors. Conditional on European markets, our findings indicate (i) superior prediction performance of PLS‐based schemes in comparison with both, a random walk and a first‐order autoregressive benchmark model, (ii) consistent profitable trading on the German and British market, (iii) profitable linear forecast combinations, (iv) the U.S. stock market is diagnosed as informationally efficient. Copyright © 2010 John Wiley & Sons, Ltd.
Markov chain models for delinquency: Transition matrix estimation and forecastingGrimshaw, Scott D.; Alexander, William P.
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.827
A Markov chain is a natural probability model for accounts receivable. For example, accounts that are ‘current’ this month have a probability of moving next month into ‘current’, ‘delinquent’ or ‘paid‐off’ states. If the transition matrix of the Markov chain were known, forecasts could be formed for future months for each state. This paper applies a Markov chain model to subprime loans that appear neither homogeneous nor stationary. Innovative estimation methods for the transition matrix are proposed. Bayes and empirical Bayes estimators are derived where the population is divided into segments or subpopulations whose transition matrices differ in some, but not all entries. Loan‐level models for key transition matrix entries can be constructed where loan‐level covariates capture the non‐stationarity of the transition matrix. Prediction is illustrated on a $7 billion portfolio of subprime fixed first mortgages and the forecasts show good agreement with actual balances in the delinquency states. Copyright © 2010 John Wiley & Sons, Ltd.
The importance of identifying different components of a mixture distribution in the prediction of field returnsHong, Yili; Meeker, William Q.
2011 Applied Stochastic Models in Business and Industry
doi: 10.1002/asmb.830
Data from a mixture of distributions with two different increasing hazard functions can behave, over some period of time, like a distribution with decreasing hazard functions. As a result, reliability predictions based on data from a mixture of units with two or more different physical designs could be seriously wrong if the pooled data are used to extrapolate in time. Thus, it is important to identify components of the mixture and do statistical inference based on the stratified data. In this paper, the importance of this principle is investigated analytically and illustrated with lifetime data on high‐voltage power transformers. From engineering knowledge, the lifetime distribution of a transformer has an increasing hazard due, largely, to insulation aging. However, data from a population of units could indicate a decreasing hazard due to a mixture of units with different designs or environmental conditions. Comparisons are made between the predictions based on the pooled‐data and stratified‐data models and the importance of correct stratification in practice is shown. Some suggestions for practitioners are also given. Copyright © 2010 John Wiley & Sons, Ltd.