A Dynamic Factor Approach to Mortality Modeling

Abstract

Longevity risk has become one of the major risks facing the insurance and pensions markets globally. The trade in longevity risk is underpinned by accurate forecasting of mortality rates. Using techniques from macroeconomic forecasting we propose a dynamic factor model of mortality that fits and forecasts age‐specific mortality rates parsimoniously. We compare the forecasting quality of this model against the Lee–Carter model and its variants. Our results show the dynamic factor model generally provides superior forecasts when applied to international mortality data. We also show that existing multifactorial models have superior fit but their forecasting performance worsens as more factors are added. The dynamic factor approach used here can potentially be further improved upon by applying an appropriate stopping rule for the number of static and dynamic factors. Copyright © 2013 John Wiley & Sons, Ltd.

The Cross‐Section of Asia‐Pacific Mortality Dynamics: Implications for Longevity Risk Sharing

“…The model performs well in our sample relative to competing models widely used in the literature, as well as more recent generalized dynamic factor models (GDFMs) (see Forni et al. ; Alessi, Barigozzi, and Capasso, ; Gao and Hu, ; French and O'Hare, ).…”

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

“…We also compare the SUTSE models with other relevant models in the mortality rate literature, such as Renshaw and Haberman () and Currie (), which capture a cohort effect, and also with the models by D'Amato et al . () and by French and O'Hare ().…”

“…Some other recent works have been proposed to predict future mortality structures and manage the longevity risk, such as Plat (), Haberman and Renshaw (, , ), Cairns (), D'Amato et al . () and French and O'Hare ().…”

“…We compare our results to some benchmarks in the literature, namely the Lee and Carter () method and its state‐space form adaptations, as well to its variants with a cohort effect, the model by D'Amato et al . () and the model by French and O'Hare (). Additionally, we discuss how the proposed model can be used by insurers and pension funds to manage the risk of declining mortality rates.…”

Inference on factor structures in heterogeneous panels

“…Another promising field of application is the prediction of mortality rates (or their first difference), where the seminal Lee-Carter model (Lee and Carter, 1992) has been extended to incorporate idiosyncratic explanatory variables as well as the traditional factor structure -see French and O'Hare (2013) and the references therein.…”