Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-29T13:46:43.176Z Has data issue: false hasContentIssue false

PROBABILITY OF SUFFICIENCY OF SOLVENCY II RESERVE RISK MARGINS: PRACTICAL APPROXIMATIONS

Published online by Cambridge University Press:  15 June 2017

Eric Dal Moro*
Affiliation:
Actuarial Reserving, SCOR 26 General Guisan-Quai, CH-8022 Zurich, Switzerland
Yuriy Krvavych
Affiliation:
Actuarial and Insurance Management Solutions, PwC UK, 7 More London Riverside, London SE1 2RT, UK E-Mail: [email protected]

Abstract

The new Solvency II Directive and the upcoming IFRS 17 regime bring significant changes to current reporting of insurance entities, and particularly in relation to valuation of insurance liabilities. Insurers will be required to valuate their insurance liabilities on a risk-adjusted basis to allow for uncertainty inherent in cash flows that arise from the liability of insurance contracts. Whilst most European-based insurers are expected to adopt the Cost of Capital approach to calculate reserve risk margin — the risk adjustment method commonly agreed under Solvency II and IFRS 17, there is one additional requirement of IFRS 17 to also disclose confidence level of the risk margin.

Given there is no specific guidance on the calculation of confidence level, the purpose of this paper is to explore and examine practical ways of estimating the risk margin confidence level measured by Probability of Sufficiency (PoS). The paper provides some practical approximation formulae that would allow one to quickly estimate the implied PoS of Solvency II risk margin for a given non-life insurance liability, the risk profile of which is specified by the type and characteristics of the liability (e.g. type/nature of business, liability duration and convexity, etc.), which, in turn, are associated with

  • the level of variability measured by Coefficient of Variation (CoV);

  • the degree of Skewness per unit of CoV; and

  • the degree of Kurtosis per unit of CoV2.

The approximation formulae of PoS are derived for both the standalone class risk margin and the diversified risk margin at the portfolio level.

Type
Research Article
Copyright
Copyright © Astin Bulletin 2017 

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

Abramowitz, M. and Stegun, I.A. (1972) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th edition. New York: Dover.Google Scholar
APRA Prudential Standards GPS 310. (2010) Audit and Actuarial Reporting and Valuation. APRA. Available at: http://www.apra.gov.au/gi/documents/gps-310-final-june-2010.pdf.Google Scholar
Babbel, D.F., Gold, J. and Merrill, C.B. (2002) Fair value of liabilities: The financial economics perspective. North American Actuarial Journal, 6 (1), 1227.CrossRefGoogle Scholar
Bateup, R. and Reed, I. (2001) Research and data analysis relevant to the development of Standard Guidelines on liability valuation for general insurance. XIII General Insurance Seminar.Google Scholar
Bohman, H. and Esscher, F. (1963) Studies in risk theory with numerical illustrations concerning distribution functions and stop loss premiums. Part I. Scandinavian Actuarial Journal, 1963 (3–4), 173225.Google Scholar
Bohman, H. and Esscher, F. (1964) Studies in risk theory with numerical illustrations concerning distribution functions and stop loss premiums. Part II. Scandinavian Actuarial Journal, 1964 (1–2), 140.CrossRefGoogle Scholar
Bühlmann, H. (2004) Multidimensional valuation. Finance, 25, 1529.Google Scholar
Christoffersen, P. (2011) Elements of Financial Risk Management, 2nd edition. Waltham, MA: Academic Press.Google Scholar
Dal Moro, E. (2013) A closed-form formula for the skewness estimation of non-life reserve risk distribution. Working Paper. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2344297.CrossRefGoogle Scholar
Dal Moro, E. (2014) An approximation of the non-life reserve risk distributions using Cornish-Fisher expansion. Working Paper. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2965384.Google Scholar
Daykin, C.D., Pentikäinen, T. and Pesonen, M. (1994) Practical Risk Theory for Actuaries. UK: Chapman & Hall.Google Scholar
Embrechts, P. and Jacobsons, E. (2016) Dependence uncertainty for aggregate risk: Examples and simple bounds. In The Fascination of Probability, Statistics and their Applications. In Honour of Ole E. Barndorff-Nielsen (eds. Podolskij, M., , S. T.A.V., Stelzer, R.), pp. 395417. Berlin: Springer.Google Scholar
Embrechts, P., Klüppelberg, C. and Mikosch, T. (2011) Modelling Extremal Events: for Insurance and Finance. UK: Springer.Google Scholar
Embrechts, P., Frey, R. and McNeil, A. (2015) Quantitative Risk Management: Concepts, Techniques and Tools, 2nd edition. Princeton, NJ: Princeton University Press.Google Scholar
FINMA. (2006) Technical Document on the Swiss Solvency Test. Available at: https://www.finma.ch/FinmaArchiv/bpv/download/e/SST_techDok_061002_E_wo_Li_20070118.pdf Google Scholar
Fisher, R.A. and Cornish, E.A. (1960) The percentile points of distributions having known cumulants. Technometrics, 5, 6369.Google Scholar
Fleishman, A.I. (1978) A method for simulating non-normal distributions. Psychometrika, 43, 521532.Google Scholar
Gijbels, I. and Herrmann, K. (2014) On the distribution of sums of random variables with copula-induced dependence. Insurance: Mathematics and Economics, 59(C), 2744.Google Scholar
Haldane, J.B.S. (1938) The approximate normalization of a class of frequency distributions. Biometrika, 29, 392404.CrossRefGoogle Scholar
IAA. (2009) Measurement of liabilities for insurance contracts: Current estimates and risk margins. The International Actuarial Association. Available at: http://www.actuaries.org/LIBRARY/Papers/IAA_Measurement_of_Liabilities_2009-public.pdf.Google Scholar
IASB. (2007) Preliminary views on insurance contracts. IASB. Available at: http://www.iasb.org.Google Scholar
Isserlis, L. (1918) On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika, 12, 134139.Google Scholar
Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, Wiley Series in Probability and Statistics, volume 381, 1st edition. New York, USA: Wiley-Interscience.Google Scholar
Kotz, S., Johnson, N. and Balakrishnan, N. (1994) Continuous Univariate Distributions, volume 1, 2nd edition. New York, USA: Wiley.Google Scholar
Krvavych, Y. (2013) Making use of internal capital models. ASTIN Colloquium, May 21–24, 2013, The Hague. Available at: http://www.actuaries.org/ASTIN/Colloquia/Hague/Papers/Krvavych.pdf.Google Scholar
Lee, Y.-S. and Lee, M.C. (1992) On the derivation and computation of the Cornish-Fisher expansion. Australian Journal of Statistics, 34, 443450.Google Scholar
Marshall, A. and Olkin, I. (2007) Life Distributions: Structure of Nonparametric, Semiparametric, and Parametric Families. Springer Series in Statistics. New York, USA: Springer.Google Scholar
Møller, T. (2004) Stochastic orders in dynamic reinsurance markets. Finance and Stochastics, 8 (4), 479499.CrossRefGoogle Scholar
Møller, T. and Steffensen, M. (2007) Market-Valuation Methods in Life and Pension Insurance. Cambridge: Cambridge University Press.Google Scholar
Nadarajah, S. (2011) The exponentiated exponential distribution: a survey. AStA Advances in Statistical Analysis, 95, 219251.Google Scholar
Nadarajah, S. and Kotz, S. (2006) The exponentiated type distributions. Acta Applicandae Mathematicae, 92, 97111.Google Scholar
Pentikäinen, T. (1987) Approximative evaluation of the distribution function of aggregate claims. ASTIN Bulletin, 17, 1539.Google Scholar
Salzmann, R., Wüthrich, M. and Merz, M. (2012) Higher moments of the claims development result in general insurance. ASTIN Bulletin, 42, 355384.Google Scholar
Sandström, A. (2011) Handbook of Solvency for Actuaries and Risk Managers. UK: Chapman & Hall.Google Scholar
SCOR. (2008) From Principle-Based Risk Management to Solvency Requirements: Analytical Framework for the Swiss Solvency Test. Switzerland: SCOR.Google Scholar
Seal, H. (1977) Approximations to risk theory's f(x, t) by means of the gamma distribution. ASTIN Bulletin, 9, 213218.CrossRefGoogle Scholar
Shmakov, S. (2011) A universal method of solving quartic equations. International Journal of Pure and Applied Mathematics, 71 (2), 251259.Google Scholar
SST. (2004) White paper of the Swiss Solvency Test. Swiss Federal Office for Private Insurance. Available at: https://www.finma.ch/FinmaArchiv/bpv/download/e/WhitePaperSST_en.pdf.Google Scholar
Strommen, S.J. (2006) Setting the levels of margins in a principle-based valuation using a cost-of-capital approach with exponential utility. The Financial Reporter, 2006 (65).Google Scholar
Taylor, G. (2006) APRA general insurance risk margins. Australian Actuarial Journal, 12 (3), 367397.Google Scholar
Wilson, E.B. and Hilferty, M. (1931) The distribution of chi-square. Proceedings of the National Academy of Science, 17, 684688.Google Scholar
Wolfram Documentation Center. Parametric statistical distributions. Available at: http://reference.wolfram.com/language/guide/ParametricStatisticalDistributions.html.Google Scholar
Wüthrich, M., Bühlmann, H. and Furrer, H. (2007) Market-Consistent Actuarial Valuation. Heidelberg: Springer.Google Scholar