Improving Lee-Carter Forecasting: Methodology and Some Results

Giovanna Apicella (), Michel M. Dacorogna (), Emilia Di Lorenzo () and Marilena Sibillo ()
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Giovanna Apicella: University of Rome “La Sapienza”
Michel M. Dacorogna: DEAR-Consulting
Emilia Di Lorenzo: University of Naples Federico II
Marilena Sibillo: University of Salerno

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 57-61 from Springer

Abstract: Abstract The aim of the paper is to improve the Lee-Carter model performance developing a methodology able to refine its predictive accuracy. Considering relevant information the discrepancies between the real data and the Lee-Carter outputs, we model a measure of the fitting errors as a Cox-Ingersoll-Ross process. A new LC model is derived, called mLC. We apply the results over a fixed prediction span and with respect to the mortality data relating to the Italian females aged 18 and 65, chosen as examples of the model application. Through the backtesting procedure within a static framework, the model mLC proves itself to outperform the LC model.

Keywords: Backtesting methods; Cox-Ingersoll-Ross process; Lee-Carter model; Out-of-sample forecasting performance (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_10

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DOI: 10.1007/978-3-319-89824-7_10

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