Showing papers by "Jens Perch Nielsen published in 2017"
TL;DR: In this paper, the authors compare the concepts underlying modern actuarial solutions to pension insurance and present two recently developed pension products (pooled annuity overlay funds and equitable income tontines) based on equitability.
Abstract: We compare the concepts underlying modern actuarial solutions to pension insurance and present two recently developed pension products—pooled annuity overlay funds (based on actuarial fairness) and equitable income tontines (based on equitability). These two products adopt specific approaches to the management of longevity risk by mutualising it among participants rather than transferring it completely to the insurer. As the market would appear to be ready for such innovations, our study seeks to establish a general framework for their introduction. We stress that the notion of actuarial fairness, which characterises pooled annuity overlay funds, enables participants to join and exit the fund at any time. Such freedom of action is a quite remarkable feature and one that cannot be matched by lifelong contracts.
15 citations
TL;DR: In this article, a new structural model for in-sample density forecasting is proposed, where the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations.
Abstract: In this paper we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then re-use the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study.
13 citations
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TL;DR: Detailed mathematical statistical theory and small sample performance are introduced via this paper and further upgraded to the new class of best one-sided cross-validation, which turns out to have excellent performance in its practical illustrations, in itssmall sample performance and in its mathematical statistical theoretical performance.
Abstract: This paper develops detailed mathematical statistical theory of a new class of cross-validation techniques of local linear kernel hazards and their multiplicative bias corrections. The new class of cross-validation combines principles of local information and recent advances in indirect cross-validation. A few applications of cross-validating multiplicative kernel hazard estimation do exist in the literature. However, detailed mathematical statistical theory and small sample performance are introduced via this paper and further upgraded to our new class of best one-sided cross-validation. Best one-sided cross-validation turns out to have excellent performance in its practical illustrations, in its small sample performance and in its mathematical statistical theoretical performance.
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TL;DR: In this article, the authors introduce the idea of smooth backfitting to survival analysis in a proportional hazard model, where they assume an underlying conditional hazard with multiplicative components, and develop asymptotic theory for the estimator.
Abstract: Smooth backfitting has proven to have a number of theoretical and practical advantages in structured regression. Smooth backfitting projects the data down onto the structured space of interest providing a direct link between data and estimator. This paper introduces the ideas of smooth backfitting to survival analysis in a proportional hazard model, where we assume an underlying conditional hazard with multiplicative components. We develop asymptotic theory for the estimator and we use the smooth backfitter in a practical application, where we extend recent advances of in-sample forecasting methodology by allowing more information to be incorporated, while still obeying the structured requirements of in-sample forecasting.