G
Gosse Alserda
Researcher at Erasmus University Rotterdam
Publications - 7
Citations - 53
Gosse Alserda is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Pension & Diseconomies of scale. The author has an hindex of 4, co-authored 7 publications receiving 40 citations. Previous affiliations of Gosse Alserda include Erasmus Research Institute of Management.
Papers
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Journal ArticleDOI
Individual pension risk preference elicitation and collective asset allocation with heterogeneity
Gosse Alserda,Benedict G. C. Dellaert,Benedict G. C. Dellaert,Laurens Swinkels,Fieke van der Lecq +4 more
TL;DR: In this paper, a tailored augmented lottery choice method was applied to elicit individual pension income risk preferences from 7894 members from five different pension plans and the results showed that member risk preferences are strongly heterogeneous and can only partially be predicted from individual and plan characteristics.
Journal ArticleDOI
X-efficiency and economies of scale in pension fund administration and investment
TL;DR: In this paper, the authors present new estimates of scale economies of pension funds and is the first that also measures pension fund X-inefficiency, and they use a unique supervisory data set which distinguishes between administrative and investment costs and apply various approaches and models.
Pension risk preferences: a personalized elicitation method and its impact on asset allocation
TL;DR: In this article, the authors propose a solution to solve the problem of concurrence of the 2.7.7 dB.0 dB.1 dB.2 dB.5 dB.
Choices in Pension Management
TL;DR: In this paper, an augmented risk preferences elicitation method was used to find strong heterogeneity in latent risk preferences, together with institutional differences, which affect the optimal asset allocation of pensions.
Journal ArticleDOI
X-efficiency and Economies of Scale in Pension Fund Administration and Investment
TL;DR: In this article, the authors discuss scale inefficiency and X-inefficiency using various approaches and models, based on a unique supervisory data set, which distinguishes between administrative and investment costs.