The impact of multiple structural changes on mortality predictions

Most mortality models proposed in recent literature rely on the standard ARIMA framework (in particular: a random walk with drift) to project mortality rates. As a result the projections are highly sensitive to the calibration period. We therefore analyse the impact of allowing for multiple structural changes on a large collection of mortality models. We find that this may lead to more robust projections for the period effect but that there is only a limited effect on the ranking of the models based on backtesting criteria, since there is often not yet sufficient statistical evidence for structural changes. However, there are cases for which we do find improvements in estimates and we therefore conclude that one should not exclude on beforehand that structural changes may have occurred.     

Stable R&D Cooperation with Spillovers

The literature on the incentives for R & D cooperation with spillovers typically deals only with the factors affecting cooperative profits. This paper focuses on the incentives to cheat and the stability of such cooperative agreements in a repeated game framework. It is shown that the stability of cooperation is influenced by the nature and magnitude of spillovers, relative to the nature and degree of product market competition. While cooperative profits are higher with large positive (exogenous, unintended) know-how spillovers, such as in fundamental research, our anslysis shows that it may be easier to sustain cooperation in areas with lower spillovers, such as applied research, because of the smaller incenfives to cheat on the initial agreement, at least when firms produce substitutes. Alternatively, the possibility of technology sharing (i.e., intended or endogenous spillovers), besides R&D coordination, not only increases cooperative profits but also reduces the incentives to defect from a cooperative equilibrium.     

Lognormal Mixed Models for Reported Claims Reserves

Abstract

Traditional claims-reserving techniques are based on so-called run-off triangles containing aggregate claim figures. Such a triangle provides a summary of an underlying data set with individual claim figures. This contribution explores the interpretation of the available individual data in the framework of longitudinal data analysis. Making use of the theory of linear mixed models, a flexible model for loss reserving is built. Whereas traditional claims-reserving techniques don’t lead directly to predictions for individual claims, the mixed model enables such predictions on a sound statistical basis with, for example, confidence regions. Both a likelihood-based as well as a Bayesian approach are considered. In the frequentist approach, expressions for the mean squared error of prediction of an individual claim reserve, origin year reserves, and the total reserve are derived. Using MCMC techniques, the Bayesian approach allows simulation from the complete predictive distribution of the reserves and the calculation of various risk measures. The paper ends with an illustration of the suggested techniques on a data set from practice, consisting of Belgian automotive third-party liability claims. The results for the mixed-model analysis are compared with those obtained from traditional claims-reserving techniques for run-off triangles. For the data under consideration, the lognormal mixed model fits the observed individual data well. It leads to individual predictions comparable to those obtained by applying chain-ladder development factors to individual data. Concerning the predictive power on the aggregate level, the mixed model leads to reasonable predictions and performs comparable to and often better than the stochastic chain ladder for aggregate data.     

Micro-level stochastic loss reserving for general insurance

The vast literature on stochastic loss reserving concentrates on data aggregated in run-off triangles. However, a triangle is a summary of an underlying data-set with the development of individual claims. We refer to this data-set as ‘micro-level’ data. Using the framework of Position Dependent Marked Poisson Processes) and statistical tools for recurrent events, a data-set is analyzed with liability claims from a European insurance company. We use detailed information of the time of occurrence of the claim, the delay between occurrence and reporting to the insurance company, the occurrences of payments and their sizes, and the final settlement. Our specifications are (semi)parametric and our approach is likelihood based. We calibrate our model to historical data and use it to project the future development of open claims. An out-of-sample prediction exercise shows that we obtain detailed and valuable reserve calculations. For the case study developed in this paper, the micro-level model outperforms the results obtained with traditional loss reserving methods for aggregate data.     

Demand-Creating R&D in a Symmetric Oligopoly

Spillovers with demand-creating research and development (R&D) activities are investigated by revisiting a widely employed market share rivalry demand structure. Positive technological spillovers may inflict positive or negative side effects on rivals and this has important implications for the effects on innovative efforts of loose or tight R&D cooperation in symmetric oligopolies. A comparison with the effects that apply with linear demand structures and implications for empirical research are also touched upon.     

Can Stock Markets Predict M&A Failure? A Study of European Transactions in the Fifth Takeover Wave

In this paper we develop various measures of M&A failure for an intra‐European sample during the fifth takeover wave: inferior long‐term stock performance, inferior operating performance, and target divestment. After documenting the extent of M&A failure, we test the relation between short‐term abnormal returns at deal announcement and M&A failure. We examine a sample where listed bidders acquire listed targets (267 deals) as well as privately‐held targets (336 deals). Our results indicate M&A failure rates up to 50% in both samples. When acquirers and targets are listed, lower M&A announcement returns are consistently and significantly associated with higher M&A failure probabilities and long‐term losses. In contrast, when targets are privately held, we find no evidence of such an association.     

Trade credit and company performance during the 2008 financial crisis

We investigate whether the 2008 financial crisis had an impact on companies’ trade credit, and whether changes in trade credit mitigated the crisis’s impact on firm profitability. We document that the availability of trade credit decreased, and that this decline is more pronounced, the higher the companies’ pre‐crisis reliance on short‐term debt. We further report evidence that the redistribution hypothesis holds during crisis periods. Finally, we show that the crisis had a negative impact on company performance, but that this impact was lower (greater) for firms that report an increase in trade receivables (payables) in crisis compared to pre‐crisis periods.     

The influence of topic involvement on mail‐survey response behavior

In this study the influence of topic involvement on mail‐survey response rate and speed was experimentally investigated. The results show that response rates for topics that are generally considered as high involvement were higher than for topics that are generally considered as low involvement. However, the speed of response did not differ between the two levels of topic involvement. Moreover, in terms of response rates an interesting interaction effect was observed: For the high‐involvement topic, the response rate showed a further positive and significant correlation with the within‐topic level of involvement, but this was not the case for the low‐involvement topic. The implications of these findings to nonresponse bias in mail survey research are discussed. © 2002 Wiley Periodicals, Inc.     

The smarter,the better?! Customer well-being,engagement, and perceptions in smart service systems

Smart service systems – that is, configurations of smart products and service providers that deliver smart services – are striving to increase the smartness of their offering, but potential consequences for customer well-being are largely overlooked. Therefore, this research investigates the impact of smartness on customer well-being (here, self-efficacy and technology anxiety) through (1) customer engagement with different smart service system actors (here, smart products and service providers) and (2) customer perceptions (here, personalization and intrusiveness perceptions) and their associated importance (here, need for personalization and intrusiveness sensitivity). A scenario-based experiment (n = 730) – which is preceded by a systematic review to conceptualize smartness – shows that customers perceive more personalization than intrusiveness in case of higher levels of smartness, resulting in customer engagement with the smart product and to some extent with the service provider. Via customer engagement with the smart product, higher levels of smartness stimulate self-efficacy, especially for customers with a high need for personalization. When customers' need for personalization is high and their intrusiveness sensitivity is low, higher levels of smartness also reduce technology anxiety via customer engagement with the smart product. Hence, the conclusion is: “The smarter, the better!”, whereby the relationship between smartness and well-being (here, self-efficacy and technological anxiety) is significantly influenced by customer heterogeneity. These findings help business practitioners in boosting customer well-being by increasing customer engagement through higher levels of smartness of their service system.