Pension fund finance and sponsoring companies

Copyright © Cambridge University Press 2012.We present empirical evidence on the funding and portfolio allocation of around 200 Dutch corporate pension funds over the period 1996-2005, with a special focus on the influence of the sponsoring firm. We find that unprofitable and small firms contribute less to their pension funds than profitable and large firms, consistent with theories of capital market imperfections. Sponsor contributions are found to be positively correlated with leverage, suggesting that tax effects play a role. Defined benefit funds invest relatively more in equity and less in bonds than their defined contribution counterparts, which is in accordance with the risk shifting theory

Pension fund finance and sponsoring companies: Empirical evidence on theoretical hypotheses

This study presents empirical evidence on the influence of sponsoring companies on the funding and portfolio allocation of pension funds, an issue on which most extant literature is theoretical. We use a unique microdataset of 550 Dutch defined benefit company pension funds and 100 sponsoring firms over 1996-2005 to test the relevance of the main theoretical hypotheses, the first paper to do so in a comprehensive manner. We find that pension funds have lower cover ratios when (1) their sponsoring company is highly leveraged, (2) the fund’s return on assets is relatively low, and (3) the sponsoring firm is small. Further, defined benefit pension funds are found to invest more in shares when their sponsoring companies are highly leveraged. These links in general suggest higher risk in the sponsor leads to correspondingly higher risk in the fund, and warrant close attention by regulators

Using a bootstrap method to choose the sample fraction in tail index estimation

Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e. the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean squared error. Unlike previous methods, prior knowledge of the second order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications.tail index;bias;bootstrap;mean squared error;optimal extreme sample fraction

On functions with small differences

AbstractAn Abel-Tauber theorem is proved and applied to multiplicative arithmetic functions

Differential trajectories of tobacco smoking in people at ultra-high risk for psychosis: Associations with clinical outcomes

Objective: People at ultra-high risk (UHR) for psychosis have a high prevalence of tobacco smoking, and rates are even higher among the subgroup that later develop a psychotic disorder. However, the longitudinal relationship between the course of tobacco smoking and clinical outcomes in UHR subjects is unknown. Methods: We investigated associations between tobacco smoking and clinical outcomes in a prospective study of UHR individuals (n = 324). Latent class mixed model analyses were used to identify trajectories of smoking severity. Mixed effects models were applied to investigate associations between smoking trajectory class and the course of attenuated psychotic symptoms (APS) and affective symptoms, as assessed using the CAARMS. Results: We identified four different classes of smoking trajectory: (i) Persistently High (n = 110), (ii) Decreasing (n = 29), (iii) Persistently Low (n = 165) and (iv) Increasing (n = 20). At two-year follow-up, there had been a greater increase in APS in the Persistently High class than for both the Persistently Low (ES = 9.77, SE = 4.87, p = 0.046) and Decreasing (ES = 18.18, SE = 7.61, p = 0.018) classes. There were no differences between smoking classes in the incidence of psychosis. There was a greater reduction in the severity of emotional disturbance and general symptoms in the Decreasing class than in the High (ES = −10.40, SE = 3.41, p = 0.003; ES = −22.36, SE = 10.07, p = 0.027), Increasing (ES = −11.35, SE = 4.55, p = 0.014; ES = −25.58, SE = 13.17, p = 0.050) and Low (ES = −11.38, SE = 3.29, p = 0.001; ES = −27.55, SE = 9.78, p = 0.005) classes, respectively. Conclusions: These findings suggests that in UHR subjects persistent tobacco smoking is associated with an unfavorable course of psychotic symptoms, whereas decrease in the number of cigarettes smoked is associated with improvement in affective symptoms. Future research into smoking cessation interventions in the early stages of psychoses is required to shine light on the potential of modifying smoking behavior and its relation to clinical outcomes.</p

On Max-Stable Processes and the Functional D-Norm

We introduce a functional domain of attraction approach for stochastic processes, which is more general than the usual one based on weak convergence. The distribution function G of a continuous max-stable process on [0,1] is introduced and it is shown that G can be represented via a norm on functional space, called D-norm. This is in complete accordance with the multivariate case and leads to the definition of functional generalized Pareto distributions (GPD) W. These satisfy W=1+log(G) in their upper tails, again in complete accordance with the uni- or multivariate case. Applying this framework to copula processes we derive characterizations of the domain of attraction condition for copula processes in terms of tail equivalence with a functional GPD. \delta-neighborhoods of a functional GPD are introduced and it is shown that these are characterized by a polynomial rate of convergence of functional extremes, which is well-known in the multivariate case.Comment: 22 page