Explaining the demand for structured financial products: survey and field experiment evidence

In many countries structured investment products are popular among retail investors. We explain the demand for these products using unique field data where we let subjects freely design their “favorite” structured product. Results suggest that the supply with capital protected products (guarantee certificates) might indeed be demand-driven. This does not seem to be the case for other product categories where marketing and sales practices might play a more important role. In a survey among financial practitioners we find furthermore that a demand for capital protected products can be explained by loss aversion and saving motifs, e.g. for buying a house.     

CDS and rating announcements: changing signaling during the crisis?

In parallel with the development of credit derivatives market, researchers have begun to explore the relationship between Credit Default Swap (CDS) market and rating events. Many papers, via classical event-study methodology, show that CDS market is able to signal future negative rating events announced by credit rating agencies. In this work, we incorporate into the event-study methodology the ability of Markov switching models in modeling state-dependent means and variances. This approach allows to get over the drawbacks of the classical methodology, which ignores the heteroscedasticity and volatility clustering often affecting financial time series. The proposed methodology is applied to study the reactions of CDS quotes to reviews for downgrading and effective downgradings announced by the three major credit rating agencies (Fitch Ratings, Moody’s, Standard and Poor’s), in order to examine if and to what extent CDS market anticipates announcements related with a company’s creditworthiness. The analysis, focusing mainly on volatility, is performed on two periods, 2004–2006 and 2007–2009, in order to verify whether a change in the signaling power of CDS quotes can be ascribed to recent financial turmoils.     

Innovation in family firms: an empirical analysis linking organizational and managerial innovation to corporate success

Family firms play a significant role in national economies worldwide, accounting e.g. for 85% of all enterprises in the OECD countries as well as for the majority of companies in Central Europe. Previous scholarly research on family firms has mostly focused on the question of how they differ from public corporations, describing family firms as being more conservative, less risk-raking, or reluctant to grow—in sum, as being less entrepreneurial than their non-family counterparts. Similarly, the existing literature often criticizes the lack of innovation in family firms. But since innovation has long been discovered as one of the key drivers to company success, it is surprising that its role in family firms has been mostly neglected in existing academic research so far. The aim of this article is therefore to study the role of (managerial and organizational) innovation in family firms compared to non-family firms on the basis of an empirical survey of 533 companies from Finland, using structural equation modelling (MPlus) for the statistical analyses.     

Transfer of risk in the newsvendor model with discrete demand

In this paper we consider the transfer of risk in a newsvendor model with discrete demand. We view the newsvendor model as a leader/follower problem where the manufacturer (leader) decides the wholesale price and the retailer (follower) decides the quantity ordered. Taking a Pareto-optimal contract as a starting point, the manufacturer wishes to design a real option contract to enhance profits. A new real option contract is said to be feasible if both parties' expected profit is at least as great as in the original contract. When demand is discrete, there are usually infinite feasible contracts that yield maximum expected profits to the manufacturer. In the paper we show that either all, some or none of these real option contracts offer an improved position for the retailer.     

A multicriteria decision analysis model for faculty evaluation

In the context of increasing demands for social and financial accountability of universities, the required implementation of transparent faculty evaluation systems constitutes a challenge and an opportunity for universities strategically aligning the activity of academic staff with the university goals. However, despite growing interest in the performance appraisal of faculty, only a few reported studies propose models that cover the full range of academic activities and the models in use are typically based on ad hoc scoring systems that lack theoretical soundness. This article approaches faculty evaluation from an innovative comprehensive perspective. Based on the concepts and methods of multiple criteria value measurement, it proposes a new faculty evaluation model that addresses the whole range of academic activities and can be applied within and across distinct scientific areas, while respecting their specificities. Constructed through a socio-technical process, the model was designed for and adopted by the Instituto Superior Técnico, the engineering school of the Technical University of Lisbon. The model has a two-level hierarchical additive structure, with top-level evaluation areas specified by second-level evaluation criteria. A bottom non-additive third level accounts for the quantitative and qualitative dimensions of academic activity related to each evaluation criterion. The model allows (a) the comparison of the performance of academic staff with performance targets reflecting the strategic policy concerns of university management; (b) the definition of the multicriteria value profile of each faculty member at the top level of the evaluation areas; (c) the computation of an overall value score for each faculty member, through an optimisation procedure that makes use of a flexible system of weights and (d) the assignment of faculty members to rating categories.     

You have the job you wanted--now what?

We've had calls from physicians who finally have the medical management job they always wanted and then a month later they wondered why they wanted it. I asked seven physician executives to come together as a group and talk to me about this issue. I also asked them what skills they needed most when they first got the job and, as time passed, which skills continued to be most useful. I have not used their names because they were open enough to talk about some very sensitive issues.     

POWER APPROXIMATIONS FOR A TWO-ECHELON INVENTORY SYSTEM USING SERVICE LEVELS

We consider a two-echelon inventory system with one warehouse and several stores. The warehouse as well as the stores are controlled by periodic review (s, S) inventory policies. We study the interrelationship between the safety stocks at the warehouse and the stores. Stockouts at the warehouse will result in supply delays to the stores and cause the lead time to be stochastic. The stores may react by increasing their safety stock. However, there is a trade-off between the safety stock at the warehouse and the safety stock at the stores. We use a service level at the warehouse to quantify the effect of warehouse stockouts on the lead time to the stores. The service level at the warehouse is considered a decision variable to find the best compromise between the various safety stocks by minimizing the overall costs. Using power approximations for the (s, S) policies, we provide an iterative procedure for adjusting the lead time distribution to the stores; this can result in substantial savings, but it doesn't guarantee the overall optimality. Numerical studies are provided to test the accuracy of approximations. The effects of the different system parameters on the inventory policy give general guidelines for use of the policies.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

Health Risk,Inequality Indexes,and Environmental Justice

Inequality indexes have long been used to analyze distributions of income. Studies have recently begun to use these tools to evaluate the equity of distributions of environmental harm. In response, issues have been raised regarding the appropriateness of using income-based measures in the context of undesirable outcomes. We begin from first principles, identifying a theoretical preference structure under which income-based tools can be appropriate for ranking distributions of “bads.” While some critiques of existing applications are valid, they are not a justification for rejecting the approach altogether. Instead, we show how standard income-based measures can be adjusted to accommodate bad outcomes. Rather than inequality indexes, we argue that equally distributed equivalents (EDEs) are well-suited for this purpose since they account for levels and dispersion of outcome distributions. The Kolm–Pollak EDE is particularly useful, having the advantage of consistently evaluating both bads and their complementary goods (e.g., mortality risk and survival probability). As an illustration, we show how these tools can inform an environmental justice analysis of a proposed Environmental Protection Agency (EPA) rule addressing indoor air pollution.     

Learning to Manage the Multirisk World

Risk assessment, perception, and management tend to focus on one risk at a time. But we live in a multirisk world. This essay in honor of the 40th anniversary of the Society for Risk Analysis (SRA) and the journal Risk Analysis suggests that we can—and have already begun to—strengthen risk analysis and policy outcomes by moving from a focus on the single to the multiple—multiple stressors, multiple impacts, and multiple decisions. This evolution can improve our abilities to assess actual risks, to confront and weigh risk-risk trade-offs and innovate risk-superior moves, and to build learning into adaptive regulation that adjusts over time. Recognizing the multirisk reality can help us understand complex systems, foresee unintended consequences, design better policy solutions, and learn to improve.