Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-27T10:19:10.740Z Has data issue: false hasContentIssue false
  • English
  • Français

Difficult risks and capital models

A report from the Extreme Events Working Party

Published online by Cambridge University Press:  29 August 2014

R. Frankland ,
S. Eshun ,
L. Hewitt ,
P. Jakhria ,
S. Jarvis ,
A. Rowe ,
A. D. Smith ,
A. C. Sharp ,
J. Sharpe  and
T. Wilkins
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper is a report from the Extreme Events Working Party. The paper considers some of the difficulties in calculating capital buffers to cover potential losses. This paper considers the reasons why a purely mechanical approach to calculating capital buffers may bot be possible or justified. A range of tools and techniques is presented to help address some of the difficulties identified.

Keywords

Capital ModellingJudgementModel and parameter riskProxy model errors
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 19 , Issue 3 , September 2014 , pp. 556 - 616
Copyright
Copyright © Institute and Faculty of Actuaries 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, J. (2012). Solvency II update for IMAP firms, Annex A, available at http://www.fsa.gov.uk/static/pubs/international/sol2-imap-letter-24-07-12.pdf Google Scholar
American International Group (2008). Economic capital modelling – results and implications, available at http://www.aig.com/Chartis/internet/US/en/ECM_0508a_tcm3171-443270.pdf and http://www.nytimes.com/2009/03/03/business/03aig.html Google Scholar
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 53, 370–418.Google Scholar
Cherubini, U., Luciano, E., Vecchiato, W. (2004). Copula Methods in Finance. Wiley Finance.Google Scholar
Cook, I.M. (2011). Using multiple catastrophe models. Institute & Faculty of Actuaries (slides), available at http://www.actuaries.org.uk/sites/all/files/documents/pdf/plenary-5-ian-cook.pdf Google Scholar
Cooke, R.M., Goossens, L.H.J. (1999). Procedures Guide for Structured Expert Judgement, June. Delft, Delft University of Technology.Google Scholar
Cowles, M.K., Carlin, B.P. (1996). Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association, 91(434), 883–904.Google Scholar
Dimson, E., Marsh, P., Staunton, M. (2002). Triumph of the Optimists. Princeton University Press.Google Scholar
European Commission (1999). Procedures Guide for Structured Expert Judgment, available at ftp://ftp.cordis.europa.eu/pub/fp5-euratom/docs/eur18820_en.pdf Google Scholar
Fenton, N., Neil, M. (2012). Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press.Google Scholar
Fortis (2008). Annual report, available at http://www.reports.fortis.com/2007/en/annualreview/riskmanagement/rarorac.html and http://news.bbc.co.uk/1/hi/business/7973748.stm Google Scholar
Frankland, R., Smith, A.D., Wilkins, T., Varnell, E., Holtham, A., Bifis, E., Eshun, S., Dullaway, D. (2009). Modelling extreme market events. A report of the benchmarking stochastic models working party British Actuarial Journal, 15, 99–201. doi:10.1017/S1357321700005468.Google Scholar
Grimmett, G.R., Stirzaker, D.R. (1982). Probability and Random Processes. Oxford, Clarendon Press.Google Scholar
Haldane, A. (2012). The dog and the Frisbee, Speech, available at http://www.bankofengland.co.uk/publications/Pages/speeches/2012/596.aspx Google Scholar
Hastings, W.K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.Google Scholar
Howie, D. (2002). Interpreting Probability. Controversies and Developments in the Early Twentieth Century. Cambridge University Press.Google Scholar
Jones, A.R., Copeman, P.J., Gibson, E.R., Line, N.J.S., Lowe, J.A., Martin, P., Matthews, P.N., Powell, D.S. (2006). A change agenda for reserving. Report of the General Insurance Reserving Issues Taskforce. British Actuarial Journal, 12(3), pp. 435–599.Google Scholar
Lazzari, S., Wong, C. (2012). Dimension Reduction and Interest Rate Forecasting. An actuarial paper presented to SIAS (junior version of the Institute) in London. SIAS, available at http://www.sias.org.uk/siaspapers/pastmeetings/view_meeting?id=SIASMeetingJuly12 Google Scholar
Meyn, S.P., Tweedie, R.L. (2009). Markov Chains and Stochastic Stability, 2nd ed. Cambridge, Cambridge University Press.Google Scholar
Michael, A., Roger, A., Peter, S., Solvency & Capital Management Research Group (2012). Expert judgement on expert judgement, UK Actuarial Profession Life Conference (Brussels).Google Scholar
Oeppen, J., Vaupel, J.W. (2002). Broken limits to life expectancy, available at http://user.demogr.mpg.de/jwv/pdf/scienceMay2002.pdf Google Scholar
Ouchi, F. (2004). A literature review on the use of expert opinion in probabilistic risk analysis, World Bank Policy Research Working Paper No. 3201, Washington (USA), February.Google Scholar
Rebonato, R. (2007). Plight of the Fortune Tellers: Why We Need to Manage Financial Risk Differently. Princeton University Press.Google Scholar
Richards, S.J., Currie, I.D. Ritchie, G.P. (2014). A value-at-risk framework for longevity trend risk. British Actuarial Journal, 19, 116–139. doi:10.1017/S1357321712000451.Google Scholar
Roberts, G.O., Rosenthal, J.S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.Google Scholar
Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Metropolis, N., Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1091.Google Scholar
Smith, A.D., Thomas, R.G. (2002). Positive theory and actuarial practice. The Actuary, available at http://www.guythomas.org.uk/pdf/posth.pdf Google Scholar
Suess, E., Trumbo, B. (2010). Introduction of Probability Simulation and Gibbs Sampling in R. Springer.Google Scholar
Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Allen Lane.Google Scholar
Tierney, L. (1994). Markov chains for exploring posterior distributions. Annals of Statistics, 22, 1701–1728.Google Scholar