Measuring Longevity Risk Through a Neural Network Lee-Carter Model

Marino, Mario; Levantesi, Susanna

doi:10.2139/ssrn.3599821

Cited by 5 publications

(1 citation statement)

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“…Other approaches that use machine learning techniques for forecasting mortality rates can be found in e.g. Richman and Wuthrich (2019); Richman and Wüthrich (2021); Perla et al (2021) that consider various types of Gaussian recurrent neural network structures, Nigri et al (2019); Marino and Levantesi (2020); Lindholm and Palmborg (2022) that consider univariate LSTM neural network, both with and without a Poisson population assumption, and Deprez et al (2017) that consider tree-based techniques.…”

Section: Introductionmentioning

confidence: 99%

Mortality forecasting using a Lexis-based state-space model

Andersson

Lindholm

2020

Ann. actuar. sci.

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A new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.

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Section: Introductionmentioning

confidence: 99%

Mortality forecasting using a Lexis-based state-space model

Andersson

Lindholm

2020

Ann. actuar. sci.

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Efficient use of data for LSTM mortality forecasting

Lindholm

Palmborg

2022

Eur. Actuar. J.

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We consider a simple long short-term memory (LSTM) neural network extension of the Poisson Lee-Carter model, with a particular focus on different procedures for how to use training data efficiently, combined with ensembling to stabilise the predictive performance. We compare the standard approach of withholding the last fraction of observations for validation, with two other approaches: sampling a fraction of observations randomly in time; and splitting the population into two parts by sampling individual life histories. We provide empirical and theoretical support for using these alternative approaches. Furthermore, to improve the stability of long-term predictions, we consider boosted versions of the Poisson Lee-Carter LSTM. In the numerical illustrations it is seen that even in situations where mortality rates are essentially log-linear as a function of calendar time, the boosted model does not perform significantly worse than a simple random walk with drift, and when non-linearities are present the predictive performance is improved. Moreover, boosting allows us to obtain reasonable model calibrations based on as few data points as 20 years.

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