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

92 Pages Posted: 1 Jun 2017

See all articles by Dorota Toczydlowska

Dorota Toczydlowska

The Department of Statistical Science, University College London

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Simon Man Chung Fung

Commonwealth Bank of Australia

Pavel V. Shevchenko

Macquarie University; Macquarie University, Macquarie Business School

Date Written: May 30, 2017

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 statespace 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.

Keywords: Stochastic Mortality Models, Demographic, Factor Model, Feature Extraction, Robust Estimation

Suggested Citation

Toczydlowska, Dorota and Peters, Gareth and Fung, Man Chung and Shevchenko, Pavel V., Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principle Components (May 30, 2017). Available at SSRN: https://ssrn.com/abstract=2977306 or http://dx.doi.org/10.2139/ssrn.2977306

Dorota Toczydlowska

The Department of Statistical Science, University College London ( email )

London

Gareth Peters (Contact Author)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Man Chung Fung

Commonwealth Bank of Australia

CBP
Sydney, NSW 2064
Australia

Pavel V. Shevchenko

Macquarie University ( email )

North Ryde
Sydney, New South Wales 2109
Australia

HOME PAGE: http://www.businessandeconomics.mq.edu.au/contact_the_faculty/all_fbe_staff/pavel_shevchenko

Macquarie University, Macquarie Business School ( email )

New South Wales 2109
Australia

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