Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-26T07:58:06.070Z Has data issue: false hasContentIssue false

Measuring Process Risk in Income Protection Insurance

Published online by Cambridge University Press:  17 April 2015

Steven Haberman
Affiliation:
Cass Business School, City University, 106 Bunhill Row, London EC1Y 8TZ, United Kingdom, Phone: 44 20 7040 8601, Fax: 44 20 7040 8899, Email: [email protected]
Zolan Butt
Affiliation:
Cass Business School, City University, 106 Bunhill Row, London EC1Y 8TZ, United Kingdom, Phone: 44 20 7040 8601, Fax: 44 20 7040 8899, Email: [email protected]
Ben Rickayzen
Affiliation:
Cass Business School, City University, 106 Bunhill Row, London EC1Y 8TZ, United Kingdom, Phone: 44 20 7040 8601, Fax: 44 20 7040 8899, Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The main objective of this paper is to measure the process error for a portfolio of independent disability insurance policies in a multiple state modelling context. We consider the calculation of premiums for a portfolio of income protection insurance policies in a stochastic environment represented both by random transitions in the underlying multiple state model and random external economic factors in the form of stochastic investment returns and inflation. We also investigate the sensitivity of the process error to the level of volatility incorporated in a given model using suitably defined risk measures. We then draw conclusions and identify possible avenues for future research.

Type
Workshop
Copyright
Copyright © ASTIN Bulletin 2004

References

Artzner, P. (1999) Application of Coherent Risk Measures to Capital Requirements in Insurance. North American Actuarial Journal 3(2), 1125.CrossRefGoogle Scholar
Artzner, P., Delbaen, F., Eber, J.-M. and Heath, D. (1997) Thinking Coherently. Risk 10 (November), 6871.Google Scholar
CMI Committee(1991) Continuous Mortality Investigation Reports No 12. Institute and Faculty of Actuaries London.Google Scholar
Cummins, J.D. (1991) Statistical and financial models of insurance pricing and the insurance firm. Journal of Risk and Insurance 58, 261302.CrossRefGoogle Scholar
Daykin, C.D., Pentikainen, T. and Pesonen, M. (1994) Practical Risk Theory for Actuaries. Chapman and Hall, London.Google Scholar
Haberman, S. (1987) Long-Term Sickness and Invalidity Benefit: Forecasting and other Actuarial Problems. Journal of Institute of Actuaries 114, 467533.CrossRefGoogle Scholar
Haberman, S., and Pitacco, E. (1999) Actuarial Models for Disability Insurance. CRC/Chapman and Hall, Boca Raton.Google Scholar
Haberman, S., Butt, Z. and Rickayzen, B.D. (2001) Multiple State Models, Simulation and Insurer Insolvency. Actuarial Research Paper No 136, City University, London.Google Scholar
Jones, B.L. (1997) Methods for the analysis of CCRC data. North American Actuarial Journal 1(2),4054.CrossRefGoogle Scholar
Marceau, E. and Gaillardetz, P. (1999) On life insurance reserves in a stochastic mortality and interest rate environment. Insurance: Mathematics and Economics 25, 261280.Google Scholar
Renshaw, A.E. and Haberman, S. (1995) On the graduation associated with a multiple state model for permanent health insurance. Insurance: Mathematics and Economics 17, 117.Google Scholar
Rickayzen, B.D. (2001) A Sensitivity Analysis of the Premiums for a Permanent Health Insurance (PHI) Model. Journal of Actuarial Practice 9, 189212.Google Scholar
Ross, S.M. (1990) A Course in Simulation. Macmillan, New York.Google Scholar
Venables, N. and Ripley, B.D. (1999) Modern applied statistics with S-Plus. Springer-Verlag, New York, Second Edition, 284.Google Scholar
Wilkie, A.D. (1995) More on a stochastic asset model for actuarial use. British Actuarial Journal 1, 777964.CrossRefGoogle Scholar