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Global Asset Liability Management

Published online by Cambridge University Press:  10 June 2011

M. A. H. Dempster ,
M. Germano ,
E. A. Medova  and
M. Villaverde
M. A. H. Dempster
Affiliation:
Centre for Financial Research, Judge Institute of Management, University of Cambridge, Cambridge CB2 1AG, U.K., Tel: +44(0)1223-339641, Fax: +44(0)1223-339652, Email: [email protected]
M. Germano
Affiliation:
Pioneer Investment Management Ltd, 5th Floor, 1 George's Quay Plaza, George's Quay, Dublin 2, Ireland., Tel: +353-1-636-4500, Fax: +353-1-636-4600, Email: [email protected]
E. A. Medova
Affiliation:
Centre for Financial Research, Judge Institute of Management, University of Cambridge, Cambridge CB2 1AG, U.K., Tel: +44(0)1223-339593, Fax: +44(0)1223-339652, Email: [email protected]
M. Villaverde
Affiliation:
Centre for Financial Research, Judge Institute of Management, University of Cambridge, Cambridge CB2 1AG, U.K., Tel: +44(0)1223-339651, Fax: +44(0)1223-339652, Email: [email protected]
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Abstract

Dynamic financial analysis (DFA) is a technique which uses Monte Carlo simulation to investigate the evolution over time of financial models of funds, complex liabilities and entire firms. Although of increasing popularity, the drawback of DFA is the dearth of systematic methods for optimising model parameters for strategic financial planning. This paper introduces strategic DFA which employs the only recently mature technology of dynamic stochastic optimisation to fill this gap. The new approach is described in terms of an illustrative case study of a joint university/industry project to create a decision support system for strategic asset liability management involving global asset classes and defined contribution pension plans. Although the application of the system described in the paper is to fund design and risk management, the approach and techniques described here are much more broadly applicable to strategic financial planning problems; for example, to insurance and reinsurance firms, to risk capital allocation in financial institutions and trading firms and to corporate investment and business development involving real options. As well as describing the mathematical and statistical models used in the case study, the paper treats econometric estimation, asset return and liability scenario generation, model specification and optimisation, system evaluation and historical backtesting. Throughout the system visualisation plays an important role.

Keywords

Dynamic Financial AnalysisGlobal Capital MarketsDynamic Stochastic OptimisationLarge Scale SystemsAsset Liability ManagementRisk ManagementDefined Contribution PensionsBenchmark PortfoliosGuaranteed Returns
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 9 , Issue 1 , 01 April 2003 , pp. 137 - 195
Copyright
Copyright © Institute and Faculty of Actuaries 2003

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References

Arbeleche, S.A. & Dempster, M.A.H. (2003). Econometric modelling for global asset liability management. Working paper, 12/03, Judge Institute of Management, University of Cambridge.Google Scholar
Birge, J.R., Dempster, M.A.H., Gassmann, H.I., Gunn, E.A., King, A.J. & Wallace, S.W. (1986). A standard input format for multiperiod stochastic linear programs. Committee on Algorithms Newsletter, 17, 1–20. Mathematical Programming Society.Google Scholar
Boender, G.C.E., van Aalst, P. & Heemskerk, F. (1998). Modelling and management of assets and liabilities of pension plans in the Netherlands. In Ziemba, & Mulvey, (1998), op. cit., 561–580.Google Scholar
Bradley, S.P. & Crane, D.B. (1972). A dynamic model for bond portfolio management. Management Science, 19, 131–151.CrossRefGoogle Scholar
Cairns, A.J.G. (2000). A multifactor model for the term structure and inflation for long-term risk management with an extension to the equities market. Working paper, Department of Actuarial Mathematics and Statistics, Heriot-Watt University.Google Scholar
Campbell, R. (2000). The economic factor model: theory and applications. Lehman Brothers presentation to Europlus Research and Management, Dublin, 31 March 2000.Google Scholar
Cariño, D.R., Kent, T., Myers, D.H., Stacy, C., Sylvanus, M., Turner, A.L., Watanabe, K. & Ziemba, W.T. (1994). The Russell-Yasuda Kasai model: An asset/liability model for a Japanese insurance company using multistage stochastic programming. Interfaces, 24, 29–49.CrossRefGoogle Scholar
Chellathurai, T. & Dravian, T. (2002). Generalized Markowitz mean-variance principles for multi-period portfolio selection problems. Proceedings of the Royal Society (Mathematical, Physical & Engineering Sciences), 458, 2571–2607.Google Scholar
Cochrane, J.H. (1997). Time series for macroeconomics and finance. Unpublished book manuscript.Google Scholar
Consigli, G. & Dempster, M.A.H. (1998). The CALM stochastic programming model for dynamic asset-liability management. In Ziemba, & Mulvey, (1998), op. cit., 464–500.Google Scholar
Dantzig, G.B. & Infanger, G. (1993). Multi-stage stochastic linear programs for portfolio optimization. Annals of Operations Research, 45, 59–76.CrossRefGoogle Scholar
Dempster, M.A.H., ed. (1980). Stochastic programming. Academic Press.Google Scholar
Dempster, M.A.H. (1988). On stochastic programming II: dynamic problems under risk. Stochastics, 25, 15–42.CrossRefGoogle Scholar
Dempster, M.A.H. (1993). CALM: A stochastic MIP model. Technical report, Department of Mathematics, University of Essex.Google Scholar
Dempster, M.A.H. (1998). Sequential importance sampling algorithms for dynamic stochastic programming. Working paper 32/98, Judge Institute of Management, University of Cambridge. Mathematical Programming (to appear).Google Scholar
Dempster, M.A.H., Hicks-Pedrón, N., Medova, E.A., Scott, J.E. & Sembos, A. (2000). Planning logistics operations in the oil industry. Journal of the Operational Research Society, 51, 1271–1288.CrossRefGoogle Scholar
Dempster, M.A.H. & Ireland, A.M. (1988). A financial expert decision support system. In Mathematical models for decision support. NATO ASI Series F48, G., Mitra, ed. Springer-Verlag, 415–440.Google Scholar
Dempster, M.A.H., Scott, J.E. & Thompson, G.W.P. (2002). Stochastic modelling and optimization using stochasticsTM. In Applications of stochastic programming. S.W., Wallace & W.T., Ziemba, eds. MPS-SIAM Series on optimization. Society for Industrial and Applied Mathematics.Google Scholar
Dempster, M.A.H., Scott, J.E. & Thompson, G.W.P. (2003). Two nonconvex problems in strategic financial planning. Working paper, Centre for Financial Research, University of Cambridge (in preparation).Google Scholar
Dempster, M.A.H. & Thompson, G.W.P. (2002). Dynamic portfolio replication using stochastic programming. In Risk management: value at risk and beyond. M. A. H., Dempster, ed. Cambridge University Press, 100–128.CrossRefGoogle Scholar
Dempster, M.A.H. & Thorlacius, A.E. (1998). Stochastic simulation of international economic variables and asset returns: the Falcon asset model. Proceedings of the 8th International AFIR Colloquium. Institute of Actuaries, London, 29–45.Google Scholar
Dert, C.L. (1995). A multi-stage stochastic programming approach to asset/liability management. PhD thesis, Econometric Institute, Erasmus University.Google Scholar
Doan, T.A. (1996). RATS users manual, Version 4.0.Google Scholar
Duval, D.B., Teeger, M.H. & , Yakoubov, (1999). The TY model, a stochastic investment model for asset liability management. Technical report, Aon Consulting, London.Google Scholar
Fourer, R., Gay, D.M. & Kernihan, B.W. (1993). AMPL: a modelling language for mathematical programming. Scientific Press.Google Scholar
Gablonsky, J. (1998). An implementation of the DIRECT algorithm. Research report, Centre for Research in Scientific Computation, North Carolina State University.Google Scholar
Garratt, A., Lee, R., Pesaran, M.H. & Shin, Y. (2000). A structural cointegrating VAR approach to macroeconometric modelling. In Econometric modelling: techniques and applications. S., Holly & M., Weale, eds. Cambridge University Press, Chapter 5.Google Scholar
Gassman, H.I. (1990). MSLiP: A computer code for the multistage stochastic linear programming problem. Mathematical Programming, 47, 407–423.CrossRefGoogle Scholar
Geyer, A., Herold, W., Kontriner, K. & Ziemba, W.T. (2001). The Innovest Austrian pension fund financial planning model InnoALM. Technical report, Siemens AG, Vienna.Google Scholar
Gill, P.E., Murray, W. & Saunders, M.A. (2002). SNOPT: An SQP algorithm for large-scale constrained optimisation. SIAM Journal of Optimization, 12, 979–1006.CrossRefGoogle Scholar
Hakansson, N.H. (1974). Convergence to isolastic utility and policy in multiperiod portfolio choice. Journal of Financial Economics, 1, 201–224.CrossRefGoogle Scholar
Hamilton, J.D. (1994). Time series analysis. Princeton University Press.CrossRefGoogle Scholar
Hicks-Pedrón, N. (1998). Model-based asset management: A comparative study. PhD thesis, Centre for Financial Research, University of Cambridge.Google Scholar
Hodrick, R. & Vassalou, M. (2002). Do we need multi-country models to explain exchange rate and interest rate and bond return dynamics. Journal of Economic Dynamics and Control, 26, 1275–1299.CrossRefGoogle Scholar
Horniman, M.D., Jobst, N.J., Lucas, C.A. & Mitra, G. (2000). Constructing efficient portfolios: alternative models and discrete constraints — a computational study. Technical report, Department of Mathematical Sciences, Brunel University.Google Scholar
Høyland, K, Kaut, M. & , Wallace (2001). A heuristic for generating scenario trees for multistage decision problems. Working paper, MoldeUniversity College, Molde, Norway.Google Scholar
Høyland, K. & Wallace, S.W. (2001). Generating scenario trees for multistage decision problems. Management Science, 47, 295–307.CrossRefGoogle Scholar
IBM (1972). Mathematical programming subsystem — extended (MPSX) and mixed integer programming (MIP) program description. Document GH19–1091–0.Google Scholar
ILOG (2000). OPL Manual.Google Scholar
Jarvis, S., Southall, F. & Varnell, E.C. (2001). Modern valuation techniques. Presented to the Staple Inn Actuarial Society, London, 6 February 2001.Google Scholar
Kallberg, J.G. & Ziemba, W.T. (1983). Comparison of alternative utility functions in portfolio selection problems. Management Science, 29, 1257–1276.CrossRefGoogle Scholar
Kaufmann, R., Gadmer, A. & Klett, R. (2001). Introduction to dynamic financial analysis. ASTIN Bulletin, 31, 214–249.CrossRefGoogle Scholar
Kusy, M. I. & Ziemba, W.T. (1986). A bank asset and liability management model. Operations Research, 34, 356–376.CrossRefGoogle Scholar
Kyriacou, M.N. (2001). Financial risk measurement and extreme value theory. PhD thesis, Centre for Financial Research, University of Cambridge.Google Scholar
Lane, M. & Hutchinson, P. (1980). A model for managing a certificate of deposit portfolio under uncertainty. In Dempster (1980), op. cit., 473–493.Google Scholar
Markowitz, H.M. (1952). Portfolio selection. Journal of Finance, 7, 77–91.Google Scholar
Meese, R. & Rogoff, K. (1983)a. Empirical exchange rate models of the seventies. Journal of International Economics, 14, 3–24.CrossRefGoogle Scholar
Meese, R. & Rogoff, K. (1983)b. The out-of-sample failure of empirical exchange rate models. In Exchange rates and international macroeconomics. J., Frenkel, ed. University of Chicago Press.Google Scholar
Merton, R.C. (1990). Continuous-time finance. Blackwell Publishers.Google Scholar
Mulvey, J.M. (1989). A surplus optimization perspective. Investment Management Review, 3, 31–39.Google Scholar
Mulvey, J.M. (1995). Financial planning via multi-stage stochastic optimization. Report SOR-94–09, Program in Statistics and Operations Research, Princeton University.Google Scholar
Mulvey, J.M. & Thorlacius, A.E. (1998). The Towers Perrin global capital market scenario generation system. In Ziemba, & Mulvey, (1998), op. cit., 286–314.Google Scholar
Mulvey, J.M. & Vladimirou, H. (1992). Stochastic network programming for financial planning problems. Management Science, 38, 1642–1664.CrossRefGoogle Scholar
Pesaran, M.H. & Schuermann, T. (2001). Modeling regional interdependencies using a global error-correcting macroeconomic model. Working paper, Trinity College, Cambridge.Google Scholar
Powell, M.J.D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal, 7, 303–307.CrossRefGoogle Scholar
Prékopa, A. (1980). Logarithmic concave measures and related topics. In Dempster (1980), op. cit., 63–82.Google Scholar
Scherer, A. (2002). Portfolio construction and risk budgeting. Risk Books.Google Scholar
Scott, J.E. (2002). Modelling and solution of large scale stochastic programmes. PhD thesis, Centre for Financial Research, University of Cambridge.Google Scholar
Shapiro, A. (2002). Dynamic sampling schemes for multistage stochastic optimization. Research report, Department of Operations Research, Georgia Institute of Technology.Google Scholar
Smith, A.D. & Speed, C. (1998). Gauge transforms in stochastic investment modelling. In Proceedings of the 8th International AFIR Colloquium, Institute of Actuaries, London.Google Scholar
Smith, A.D. (1996). How actuaries can use financial economics. British Actuarial Journal, 2, 1057–1193.CrossRefGoogle Scholar
Steinbach, M.C. (1999). Markowitz revisited: single period and multi-period mean-variance models. Technical report SC 99–30, Konrad-Zuse-Zentrum för Informationstechnik Berlin.Google Scholar
Vanderbei, R. (2002). Linear programming: foundations and extensions. Princeton University Press.Google Scholar
Villaverde, M. (2003). Global fund management using stochastic optimization. Working paper 43/03, Judge Institute of Management, University of Cambridge.Google Scholar
White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50, 1–25.CrossRefGoogle Scholar
Wilkie, A.D. (1986). A stochastic investment model for actuarial use. Transactions of the Faculty of Actuaries, 39, 391–403.Google Scholar
Wilkie, A.D. (1995). More on a stochastic asset model for actuarial use. British Actuarial Journal, 1, 777–964.CrossRefGoogle Scholar
Wilkie, A.D. (2000). The long term outlook for asset returns. Presented to the Inquire Europe Conference, Venice, 23 October, 2000.Google Scholar
Zenios, S.A. (1998). Asset and liability management under uncertainty for fixed income liabilities. In Ziemba, & Mulvey, (1998), op. cit., 537–560.Google Scholar
Ziemba, W.T. & Mulvey, J.M., eds. (1998). Worldwide asset and liability modelling. Cambridge University Press.Google Scholar