Basis risk modelling: a cointegration-based approach. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Salhi, Yahia; Loisel, Stéphane
• Is Part Of: Statistics. Volume 51:Issue 1(2017)
• Page Start: 205
• Page End: 221
• Publication Date: 2017-01-02
• Subjects: Longevity risk -- national and insured mortality rates -- cointegration -- basis risk; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2016.1259806
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: In this paper we propose a multivariate approach for forecasting pairwise mortality rates of related populations. The need for joint modelling of mortality rates is analysed using a causality test. We show that for the datasets considered, the inclusion of national mortality information enhances predictions on its subpopulations. The investigated approach links national population mortality to that of a subset population, using an econometric model that captures a long-term relationship between the two mortality dynamics. This model does not focus on the correlation between the mortality rates of the two populations, but rather their long-term behaviour, which suggests that the two times series cannot wander off in opposite directions for long before mean reverting, which is consistent with biological reasoning. The model can additionally capture short-term adjustments in the mortality dynamics of the two populations. An empirical comparison of the forecast of one-year death probabilities for policyholders is performed using both a classical factor-based model and the proposed approach. The robustness of the model is tested on mortality rate data for England and Wales, alongside the Continuous Mortality Investigation assured lives dataset, representing the subpopulation.