Previous Article in Journal
Robust Estimation of Value-at-Risk through Distribution-Free and Parametric Approaches Using the Joint Severity and Frequency Model: Applications in Financial, Actuarial, and Natural Calamities Domains
Previous Article in Special Issue
Actuarial Applications and Estimation of Extended CreditRisk+
Article Menu

Export Article

Open AccessArticle
Risks 2017, 5(3), 42; doi:10.3390/risks5030042

Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components

1
Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
2
Man Institute of Quantitative Finance, University of Oxford, Oxford OX1 3BD, UK
3
CSIRO, Canberra, ACT 2601, Australia
4
Department of Applied Finance and Actuarial Studies, Macquarie University, Sydney, NSW 2109, Australia
*
Author to whom correspondence should be addressed.
Received: 7 February 2017 / Revised: 31 May 2017 / Accepted: 17 July 2017 / Published: 27 July 2017
(This article belongs to the Special Issue Ageing Population Risks)

Abstract

In this study we develop a multi-factor extension of the family of Lee-Carter stochastic mortality models. We build upon the time, period and cohort stochastic model structure to extend it to include exogenous observable demographic features that can be used as additional factors to improve model fit and forecasting accuracy. We develop a dimension reduction feature extraction framework which (a) employs projection based techniques of dimensionality reduction; in doing this we also develop (b) a robust feature extraction framework that is amenable to different structures of demographic data; (c) we analyse demographic data sets from the patterns of missingness and the impact of such missingness on the feature extraction, and (d) introduce a class of multi-factor stochastic mortality models incorporating time, period, cohort and demographic features, which we develop within a Bayesian state-space estimation framework; finally (e) we develop an efficient combined Markov chain and filtering framework for sampling the posterior and forecasting. We undertake a detailed case study on the Human Mortality Database demographic data from European countries and we use the extracted features to better explain the term structure of mortality in the UK over time for male and female populations when compared to a pure Lee-Carter stochastic mortality model, demonstrating our feature extraction framework and consequent multi-factor mortality model improves both in sample fit and importantly out-off sample mortality forecasts by a non-trivial gain in performance. View Full-Text
Keywords: mortality modelling; cohort models; factor models; state-space models; Bayesian inference; Markov chain Monte Carlo; features extraction; robust dimensionality reduction mortality modelling; cohort models; factor models; state-space models; Bayesian inference; Markov chain Monte Carlo; features extraction; robust dimensionality reduction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Toczydlowska, D.; Peters, G.W.; Fung, M.C.; Shevchenko, P.V. Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components. Risks 2017, 5, 42.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top