Econometric models of the Eleventh District Cost of Funds Index

The Eleventh District Cost of Funds Index (COFI) is a popular index for pricing adjustable-rate mortgages. COFI is calculated from the interest expenses incurred by thrifts when raising funds. It is a mixture of current and past interest rates on many different financial instruments. COFI can be modelled well with simple econometric models. Commonly used, simple COFI models are compared using a method developed by Hendry (1989). Some of these models, which appear to fit the data well, have nonrobust parameters, significant serial correlation, and heteroscedastic errors. These poorly specified models may lead to systematic mispricing of COFI mortgages. Once a robust econometric model is chose, the lagged adjustment of COFI to movements in interest rates can be incorporated into mortgage pricing models.     

The Risk and Predictability of International Equity Returns

We investigate predictability in national equity market returns,and its relation to global economic risks. We show how to consistentlyestimate the fraction of the predictable variation that is capturedby an asset pricing model for the expected returns. We use amodel in which conditional betas of the national equity marketsdepend on local information variables, while global risk premiadepend on global variables. We examine single- and multiple-betamodels, using monthly data for 1970 to 1989. The models capturemuch of the predictability for many countries. Most of thisis related to time variation in the global risk premia.     

Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors that is free from potential distortion resulting from the analysis of preference data confounded with the effects of consumer-specific brand unfamiliarity. An application based on preference and familiarity ratings for ten luxury car models collected from 240 consumers who intended to buy a luxury car within a designated time frame is presented. The results are compared with those obtained from MDPREF, a popular metric vector MDS model used for the scaling of preference data. In particular, we find that the consumer preference vectors obtained from the proposed methodology are substantially different in orientation from those estimated by the MDPREF model. The implications of the methodology are discussed.The authors gratefully acknowledge helpful comments from the editor and two anonymous reviewers, and also from Michael D. Johnson and Robert J. Meyer. They also thank Michael Kusnick and Robert Kleinbaum for their assistance in conducting the survey.     

How call center location impacts expectations of service from reputable versus lesser known firms

Businesses are increasingly turning to call service centers located abroad to provide customer support. Although the country-of-origin literature as well as other reports may lead one to believe that consumers will expect offshore call centers to deliver poorer service, call center location is simply one cue which consumers can utilize to form expectations. This research investigates how two factors (location and reputation) impact a consumer's expectations regarding an upcoming service encounter. We employ information integration theory to examine how these two cues are combined. Results from two experiments demonstrate that call center location does not impact pre-encounter expectations if the firm is reputable. If, however, the firm is less known, consumers anticipate being less satisfied if they believe the call center is located in a nation dissimilar to the U.S. (e.g., India or the Philippines) versus in the U.S. or similar to the U.S. (e.g., England). Belief about training provided by the firm is shown to be the key mediator.     

Initiating events and independent protection layers

Layer of protection analysis (LOPA) is a semiquantitative tool for analyzing and assessing process risk. The tool has grown greatly in popularity and usefulness since the publication of the first CCPS/AIChE guidebook on the subject, Layer of Protection Analysis, Simplified Process Risk Assessment (LOPA). This article is a summary of a new CCPS guideline book that includes the following:

• Initiating events (IEs).
• Independent protection layers (IPLs).
• Criteria for identifying the value of each prospective IE and prospective IPL.
• Example IE and IPL data.

This book is a necessary reference for those applying the LOPA methodology. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2009     

Recognizing Business Ethics: Practical and Ethical Challenges in Awarding Prizes for Good Corporate Behaviour

There seems to be a proliferation of prizes and rankings for ethical business over the past decade. Our principal aims in this article are twofold: to initiate an academic discussion of the epistemic and normative stakes in business-ethics competitions; and to help organizers of such competitions to think through some of these issues and the design options for dealing with them. We have been able to find no substantive literature – academic or otherwise – that addresses either of these two broad topics and audiences. Our modest aim, therefore, is to suggest an agenda of issues, and to begin to explore and analyse some of the possible arguments for and against various philosophical or practical solutions. Part I explores the challenges facing a prize-organizing committee, including problems derived from what Rawls calls the “fact of pluralism” in democratic societies (reasonable people will always disagree over some basic values, including those relevant to evaluating business practices), and epistemic issues about how we can justify qualitative judgments on the basis of incomplete quantitative data. We also try to identify risks and opportunity costs for ethics-prize granters. In Part II we spell out (a) a range of design options and (b) some advice about how any particular prize-awarding committee might select among these options to best achieve its goals (which typically involve highlighting and publicizing best practices for ethical business).     

Can we send her there? Maximizing the success of western women on global assignments

This paper examines factors affecting the performance of Western women on global assignments. Four categories of causal agents that affect female expatriates' success are: their individual characteristics, their organizations, their families, and the host nationals with whom they work. Along with these factors, the paper offers 15 strategies for multinational organizations to maximize the likelihood of success of their female expatriates.     

Testing machine learning systems in real estate

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.