Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T21:34:33.389Z Has data issue: false hasContentIssue false

Dependent Loss Reserving using Copulas

Published online by Cambridge University Press:  09 August 2013

Peng Shi
Affiliation:
Division of Statistics, Northern Illinois University, DeKalb, Illinois 60115USA, E-mail: [email protected]
Edward W. Frees
Affiliation:
School of Business, University of Wisconsin, Madison, Wisconsin 53706USA, E-mail: [email protected]

Abstract

Modeling dependencies among multiple loss triangles has important implications for the determination of loss reserves, a critical element of risk management and capital allocation practices of property-casualty insurers. In this article, we propose a copula regression model for dependent lines of business that can be used to predict unpaid losses and hence determine loss reserves. The proposed method, relating the payments in different run-off triangles through a copula function, allows the analyst to use flexible parametric families for the loss distribution and to understand the associations among lines of business. Based on the copula model, a parametric bootstrap procedure is developed to incorporate the uncertainty in parameter estimates.

To illustrate this method, we consider an insurance portfolio consisting of personal and commercial automobile lines. When applied to the data of a major US property-casualty insurer, our method provides comparable point prediction of unpaid losses with the industry's standard practice, chain-ladder estimates. Moreover, our flexible structure allows us to easily compute the entire predictive distribution of unpaid losses. This procedure also readily yields accident year reserves, calendar year reserves, as well as the aggregate reserves. One important implication of the dependence modeling is that it allows analysts to quantify the diversification effects in risk capital analysis. We demonstrate these effects by calculating commonly used risk measures, including value at risk and conditional tail expectation, for the insurer's combined portfolio of personal and commercial automobile lines.

Type
Research Article
Copyright
Copyright © International Actuarial Association 2011

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

Ajne, B. (1994) Additivity of chain-ladder projections. ASTIN Bulletin 24(2), 311318.Google Scholar
Braun, C. (2004) The prediction error of the chain ladder method applied to correlated run-off triangles. ASTIN Bulletin 34(2), 399424.Google Scholar
Brehm, P. (2002) Correlation and the aggregation of unpaid loss distributions. In CAS Forum, 123.Google Scholar
de Alba, E. (2006) Claims reserving when there are negative values in the runoff triangle: Bayesian analysis using the three-parameter log-normal distribution. North American Actuarial Journal 10(3), 4559.Google Scholar
de Alba, E. and Nieto-Barajas, L. (2008) Claims reserving: a correlated Bayesian model. Insurance: Mathematics and Economics 43(3), 368376.Google Scholar
de Jong, P. (2010) Modeling dependence between loss triangles using copula. Working Paper.Google Scholar
de Jong, P. and Heller, G. (2008) Generalized Linear Models for Insurance Data. Cambridge University Press.Google Scholar
England, P. and Verrall, R. (2002) Stochastic claims reserving in general insurance. British Actuarial Journal 8(3), 443518.Google Scholar
Frees, E. Shi, P. and Valdez, E. (2009) Actuarial applications of a hierarchical insurance claims model. ASTIN Bulletin 39(1), 165197.Google Scholar
Frees, E. and Valdez, E. (2008) Hierarchical insurance claims modeling. Journal of the American Statistical Association 103(484), 14571469.Google Scholar
Frees, E. and Wang, P. (2005) Credibility using copulas. North American Actuarial Journal 9(2), 3148.CrossRefGoogle Scholar
Frees, E. and Wang, P. (2006) Copula credibility for aggregate loss models. Insurance: Mathematics and Economics 38(2), 360373.Google Scholar
Genest, C., Rémillard, B. and Beaudoin, D. (2009) Goodness-of-fit tests for copulas: a review and a power study. Insurance: Mathematics and Economics 44(2), 199213.Google Scholar
Hess, K., Schmidt, K. and Zocher, M. (2006) Multivariate loss prediction in the multivariate additive model. Insurance: Mathematics and Economics 39(2), 185191.Google Scholar
Holmberg, , Randall, D. (1994) Correlation and the measurement of loss reserve variability. In CAS Forum, 1247.Google Scholar
Joe, H. (1997) Multivariate Models and Dependence Concepts. Chapman & Hall.Google Scholar
Kirschner, G., Kerley, C. and Isaacs, B. (2002) Two approaches to calculating correlated reserve indications across multiple lines of business. In CAS Forum, 211246.Google Scholar
Kirschner, G., Kerley, C. and Isaacs, B. (2008) Two approaches to calculating correlated reserve indications across multiple lines of business. Variance 2(1), 1538.Google Scholar
Kremer, E. (1982) IBNR claims and the two way model of ANOVA. Scandinavian Actuarial Journal 1, 4755.Google Scholar
Landsman, Z. and Valdez, E. (2003) Tail conditional expectations for elliptical distributions. North American Actuarial Journal 7(4), 5571.CrossRefGoogle Scholar
Mack, T. (1991) A simple parametric model for rating automobile insurance or estimating IBNR claims reserves. ASTIN Bulletin 21(1), 93109.Google Scholar
Mack, T. (1993) Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin 23(2), 213225.Google Scholar
Merz, M. and Wüthrich, M. (2008) Prediction error of the multivariate chain ladder reserving method. North American Actuarial Journal 12(2), 175197.Google Scholar
Merz, M. and Wüthrich, M. (2009a) Combining chain-ladder and additive loss reserving methods for dependent lines of business. Variance 3(2), 270291.Google Scholar
Merz, M. and Wüthrich, M. (2009b) Prediction error of the multivariate additive loss reserving method for dependent lines of business. Variance 3(1), 131151.Google Scholar
Merz, M. and Wüthrich, M. (2010) Paid-incurred chain claims reserving method. Insurance: Mathematics and Economics 46(3), 568579.Google Scholar
Meyers, G. (2009) Stochastic loss reserving with the collective risk model. Variance 3(2), 239269.Google Scholar
Nelsen, R. (2006) An Introduction to Copulas. Springer.Google Scholar
Renshaw, A. and Verrall, R. (1998) A stochastic model underlying the chain-ladder technique. British Actuarial Journal 4(4), 903923.Google Scholar
Schmidt, K. (2006) Optimal and additive loss reserving for dependent lines of business. In CAS Forum, 319351.Google Scholar
Shi, P. and Frees, E. (2010) Long-tail longitudinal modeling of insurance company expenses. Insurance: Mathematics and Economics 47(3), 303314.Google Scholar
Sun, J., Frees, E. and Rosenberg, M. (2008) Heavy-tailed longitudinal data modelling using copulas. Insurance: Mathematics and Economics 42(2), 817830.Google Scholar
Taylor, G. (2000) Loss Reserving: An Actuarial Perspective. Kluwer Academic Publishers.CrossRefGoogle Scholar
Taylor, G. and McGuire, G. (2007) A synchronous bootstrap to account for dependencies between lines of business in the estimation of loss reserve prediction error. North American Actuarial Journal 11(3), 70.Google Scholar
Wright, T. (1990) A stochastic method for claims reserving in general insurance. Journal of the Institute of Actuaries 117, 677731.Google Scholar
Wüthrich, M. and Merz, M. (2008) Stochastic Claims Reserving Methods in Insurance. John Wiley & Sons.Google Scholar
Zellner, A. (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57(298), 348368.Google Scholar
Zhang, Y. (2010) A general multivariate chain ladder model. Insurance: Mathematics and Economics 46(3), 588599.Google Scholar