Skip to main content
Log in

A BSDE approach to risk-based asset allocation of pension funds with regime switching

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

An asset allocation problem of a member of a defined contribution (DC) pension fund is discussed in a hidden, Markov regime-switching, economy using backward stochastic differential equations, (BSDEs). A risk-based approach is considered, where the member selects an optimal asset mix with a view to minimizing the risk described by a convex risk measure of his/her terminal wealth. Firstly, filtering theory is adopted to transform the hidden, Markov regime-switching, economy into one with complete observations and to develop, (robust), filters for the hidden Markov chain. Then the optimal asset allocation problem of the member is formulated as a two-person, zero-sum stochastic differential game between the member and the market in the economy with complete observations. The BSDE approach is then used to solve the game problem and to characterize the saddle point of the game problem. An explicit expression for the optimal asset mix is obtained in the case of a convex risk measure with quadratic penalty and it can be considered a generalized version of the Merton ratio. An explicit expression for the optimal strategy of the market is also obtained, which leads to a risk-neutral wealth dynamic and may provide some insights into asset pricing in the economy with inflation risk and regime-switching risk. Numerical examples are provided to illustrate financial implications of the BSDE solution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. Expected Shortall is also called conditional tail expectation and conditional VaR. If the loss distribution is continuous, expected shortfall is subadditive. When the loss distribution is discrete, expected shortfall is subadditive after some adjustments for discontinuities.

References

  • Artzner, P., Delbaen, F., Eber, J., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

    Article  Google Scholar 

  • Basak, S., & Shapiro, A. (2001). Value-at-risk based risk management: optimal policies and asset prices. The Review of Financial Studies, 14, 371–405.

    Article  Google Scholar 

  • BIS, Basel II (2006). International convergence of capital measurement and capital standards: a revised framework—comprehensive version, Available from http://bis.org/publ/bcbs128.pdf, 17 May 2010.

  • Bodie, Z., Merton, R. C., & Samuelson, W. (1992). Labor supply flexibility and portfolio choice in a life-cycle model. Journal of Economic Dynamics & Control, 16(3–4), 427–449.

    Article  Google Scholar 

  • Boyle, P. P., & Lin, X. (1997). Optimal portfolio selection with transaction costs. North American Actuarial Journal, 1(2), 27–39.

    Google Scholar 

  • Clark, J. M. C. (1978). The design of robust approximations to the stochastic differential equations for nonlinear filtering. In J. K. Skwirzynski (Ed.), Communications systems and random process theory (pp. 721–734). Amsterdam: Sijthoff and Noorhoff.

    Chapter  Google Scholar 

  • Constantinides, G. M. (1990). Habit formation: a resolution of the equity premium puzzle. Journal of Political Economy, 98, 519–543.

    Article  Google Scholar 

  • De Scheemaekere, X. (2008). Risk indifference pricing and backward stochastic differential equations (CEB Working Paper No. 08/027). September 2008, Solvay Business School, Brussels, Belgium.

  • Delbaen, F., Peng, S., & Rosazza Gianin, E. (2008). Representation of the penalty function of a dynamic convex risk measure. In: Princeton Conference, July, Torino.

  • Elliott, R. J. (1976). The existence of value in stochastic differential games. SIAM Journal on Control and Optimization, 14, 85–94.

    Article  Google Scholar 

  • Elliott, R. J. (1982). Stochastic calculus and applications. Berlin: Springer.

    Google Scholar 

  • Elliott, R. J. (1993). New finite-dimensional filters and smoothers for noisily observed Markov chains. IEEE Transactions on Information Theory, 39, 265–271.

    Article  Google Scholar 

  • Elliott, R. J., Aggoun, L., & Moore, J. B. (1994). Hidden Markov models: estimation and control. Berlin: Springer.

    Google Scholar 

  • Elliott, R. J., Hunter, W. C., & Jamieson, B. M. (1998). Drift and volatility estimation in discrete time. Journal of Economic Dynamics & Control, 22(2), 209–218.

    Article  Google Scholar 

  • El Karoui, N., Peng, S., & Quenez, M. C. (1997). Backward stochastic differential equations in finance. Mathematical Finance, 7, 1–71.

    Article  Google Scholar 

  • Föllmer, H., & Schied, A. (2002). Convex measures of risk and trading constraints. Finance and Stochastics, 6, 429–447.

    Article  Google Scholar 

  • Frittelli, M., & Rosazza-Gianin, E. (2002). Putting order in risk measures. Journal of Banking & Finance, 26, 1473–1486.

    Article  Google Scholar 

  • Friedman, A. (1975). Stochastic differential equations and applications. United States: Dover.

    Google Scholar 

  • Gerber, H. U., & Shiu, E. S. W. (2000). Investing for retirement: optimal capital growth and dynamic asset allocation. North American Actuarial Journal, 4(2), 42–62.

    Google Scholar 

  • International Organisation of Pension Supervisors (2011). OECD/IOPS good practices for pension funds’ risk management systems. OECD.

  • Kallianpur, G. (1980). Stochastic filtering theory. Berlin: Springer.

    Google Scholar 

  • Korn, R., Siu, T. K., & Zhang, A. (2011). Asset allocation for a DC pension fund under regime-switching environment. European Actuarial Journal, 1, 361–377.

    Article  Google Scholar 

  • Lipster, R., & Shiryaev, A. N. (2003). Statistics of random processes. Berlin: Springer.

    Google Scholar 

  • Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • Mataramvura, S., & Øksendal, B. (2007). Risk minimizing portfolios and HJB equations for stochastic differential games. Stochastics, 80, 317–337.

    Google Scholar 

  • Merton, R. C. (1971). Optimum consumption and portfolio rules in a continuous-time model. Journal of Economic Theory, 3, 373–413.

    Article  Google Scholar 

  • Merton, R. C. (2006). Observations on innovation in pension fund management in the impending future. PREA Quarterly, (winter 2006), pp. 61–67.

  • Peek, J., Reuss, A., & Scheuenstuhl, G. (2008). Evaluating the impact of risk based funding requirements on pension funds. Financial market trends. OECD.

  • Rogers, L. C. G., & William, D. (1987). Diffusions, Markov processes and martingales. Chichester: Wiley.

    Google Scholar 

  • Samuelson, P. A. (1969). Lifetime portfolio selection by dynamic stochastic programming. Review of Economics and Statistics, 51, 239–246.

    Article  Google Scholar 

  • Samuelson, P. A. (1990). Asset allocation could be dangerous to your health. Journal of Portfolio Management, Spring, 5–8.

  • Samuelson, P. A. (1997). How best to flip-flop if you must: integer dynamic programming for either-or. Journal of Risk and Uncertainty, 15, 183–190.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2008). Supply chain risk, simulation, and vendor selection. International Journal of Production Economics, 114(2), 646–655.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2009a). Introduction to the special section on “Optimizing risk management: methods and tools”. Human and Ecological Risk Assessment, 15(2), 220–226.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2009b). Enterprise risk management: small business scorecard analysis, production planning and control. The Management of Operations, 20(4), 362–369.

    Google Scholar 

  • Wu, D., & Olson, D. L. (2010a). Enterprise risk management: a DEA VaR approach in vendor selection. International Journal of Production Research, 48(16), 4919–4932.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2010b). Enterprise risk management: coping with model risk in a large bank. Journal of the Operational Research Society, 61, 179–190.

    Article  Google Scholar 

  • Wu, D., & Olson, D. L. (2010c). Introduction to special section on “Risk and technology”. Technological Forecasting & Social Change, 77, 837–839.

    Article  Google Scholar 

  • Wu, D., Kefan, X., Gang, C., & Olson, D. L. (2010a). A risk analysis model in concurrent engineering product development. Risk Analysis, 30(9), 1440–1453.

    Article  Google Scholar 

  • Wu, D., Kefan, X., Hua, L., Shi, Z., & Olson, D. L. (2010b). Modeling technological innovation risks of an entrepreneurial team using system dynamics: an agent-based perspective. Technological Forecasting & Social Change, 77, 857–869.

    Article  Google Scholar 

  • Zhang, A. (2008). Stochastic optimization in finance and life insurance: applications of the martingale method. PhD Thesis, Department of Mathematics, University of Kaiserslautern, Germany.

Download references

Acknowledgement

I would like to thank the editor and the two anonymous referees for their helpful comments. I would also like to acknowledge the Discovery Grant from the Australian Research Council (ARC) (Project No.: DP1096243).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tak Kuen Siu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Siu, T.K. A BSDE approach to risk-based asset allocation of pension funds with regime switching. Ann Oper Res 201, 449–473 (2012). https://doi.org/10.1007/s10479-012-1211-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-012-1211-5

Keywords

Navigation