Asset-Liability Management under time-varying Investment Opportunities

Ferstl, Robert and Weissensteiner, Alex (2009): Asset-Liability Management under time-varying Investment Opportunities.

Abstract

In this paper, we propose multi-stage stochastic linear programming for asset-liability management under time-varying investment opportunities. We use a first-order unrestricted vector autoregressive process to model predictability in the asset returns and the state variables, where - additional to equity returns and dividend-price ratios - Nelson/Siegel parameters are included to account for the evolution of the yield curve. As objective function we minimize conditional value at risk of the shareholder value, i.e. the difference between the mark-to-market value of (financial) assets and the present value of future liabilities. Our results indicate strong hedging demands to mitigate interest rate risks.

• Item Type: MPRA Paper
• Language: English
• Keywords: predictability, stochastic programming, scenario generation, VAR process
• Subjects: G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis

References

Ang, A. and G. Bekaert (2007). Stock Return Predictability: Is it There? Review of Finanical Studies 20, 651–707.

Artzner, P., F. Delbaen, J.-M. Eber, and D. Heath (1999, Jul.). Coherent Measures of Risk. Mathematical Finance 9(3), 203–228.

Barberis, N. C. (2000). Investing for the Long Run when Returns are Predictable. The Journal of Finance 55, 225–264.

Bertocchi, M., R. Giacometti, and S. A. Zenios (2005). Risk Factor Analysis and Portfolio Immunization in the Corporate Bond Market. European Journal of Operational Research 161, 348–363.

Boender, G., C. Dert, F. Heemskerk, and H. Hoek (2005). Handbook of Asset and Liability Management, Chapter 18: A Scenario Approach of ALM, pp. 829–861. Elsevier.

Brandt, M. W., A. Goyal, P. Santa-Clara, and J. R. Stroud (2005). A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability. The Review of Financial Studies 18(3), 831–873.

Brennan, M. J., E. S. Schwartz, and R. Lagnado (1997). Strategic Asset Allocation. Journal of Economic Dynamics and Control 21, 1377–1403.

Campbell, J. Y., G. Chacko, J. Rodriguez, and L. M. Viceira (2004). Strategic Asset Allocation in a Continuous-Time VAR Model. Journal of Economic Dynamics and Control 28, 2195–2214.

Campbell, J. Y., Y. L. Chan, and L. M. Viceira (2003). A Multivariate Model of Strategic Asset Allocation. Journal of Financial Economics 67, 41–80.

Campbell, J. Y., A. W. Lo, and A. C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton University Press.

Campbell, J. Y. and R. J. Shiller (1988). The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. Rewiew of Financial Studies 1(3), 195–228.

Campbell, J. Y. and S. B. Thompson (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the historical Average? The Review of Financial Studies 21(4), 2008.

Campbell, J. Y. and L. M. Viceira (1999). Consumption and Portfolio Decisions when Expected Returns are Time Varying. Quarterly Journal of Economics 114, 433–495.

Carino, D. R. and W. T. Ziemba (1994). The Russell-Yasuda-Kasai Model: An Asset/Liability Model for a Japanese Insurance Company Using Multistage Stochastic Programming. Interfaces 24 (1), 29–49.

Cochrane, J. and M. Piazzesi (2005). Bond Risk Premia. American Economic Review 95(1), 138–160.

Dempster, M. A. H., M. Germano, E. A. Medova, and M. Villaverde (2003). Global Asset Liability Management. British Actuarial Journal 9, 137–216.

Detemple, J. B., R. Garcia, and M. Rindisbacher (2003). A Monte Carlo Method for Optimal Portfolios. Journal of Finance 58, 401–446.

Diebold, F. X. and C. Li (2006). Forecasting the Term Structure of Government Bond Yields. Journal of Econometrics 130, 337–364.

Geyer, A., M. Hanke, and A. Weissensteiner (2009). Scenario Trees, Ar- bitrage, and Multi-Asset ALM Models. Working Paper available on http://ssrn.com/abstract=1386222.

Geyer, A. and W. T. Ziemba (2008). The Innovest Austrian Pension Fund Financial Planning Model InnoALM. Operations Research 56(4), 797–810.

Gondzio, J. and R. Kouwenberg (2001). High Performance Computing for Asset-Liability Management. Operations Research 49, 879–891.

Goyal, A. and I. Welch (2008). A Comprehensive Look at the Empirical Performance of Equity Premium Prediction. The Review of Financial Studies 21(4), 1455–1508.

Grinold, R. C. and R. N. Kahn (2000). The Efficiency Gains of Long-short Investing. Financial Analysts Journal 56(6), 40–53.

Heitsch, H. and W. Römisch (2003). Scenario Reduction Algorithms in Stochastic Programming. Computational Optimization and Applications 24, 187–206.

Hoyland, K., M. Kaut, and S. W. Wallace (2003, Feb). A Heuristic for Moment-Matching Scenario Generation. Computational Optimization and Applications 24(2-3), 169–185.

Høyland, K. and S. W. Wallace (2001). Generating Scenario Trees for Multistage Decision Problems. Management Science 47(2), 295–307.

Johnson, G., S. Ericson, and V. Srimurthy (2007, Fall). An Empirical Analysis of 130/30 Strategies: Domestic and International 130/30 Strategies Add Value Over Long-Only Strategies. The Journal of Alternative Investments 10(2), 31–42.

Keim, D. and R. F. Stambaugh (1986). Predicting Returns in the Stock and Bond Markets. Journal of Financial Economics 14(5), 357–390.

Kim, T. S. and E. Omberg (1996). Dynamic Nonmyopic Portfolio Behavior. The Review of Financial Studies 9(1), 141–161.

Klaassen, P. (2002). Comment on “Generating Scenario Trees for Multistage Decision Problems”. Management Science 48, 1512–1516.

Koijen, R. S., T. E. Nijman, and B. J. M. Werker (2009). When Can Life-cylce Investors Benefit from Time-varying Bond Risk Premia? The Review of Financial Studies forthcoming. Lütkepohl, H. (2005). Introduction to Multiple Time Series Analysis. Springer.

Merton, R. C. (1969). Lifetime Portfolio Selection Under Uncertainty: The Continuous-time Case. Review of Economics and Statistics 51, 247–257.

Merton, R. C. (1971). Optimum Consumption and Portfolio Rules in a Continuous-Time Model. Journal of Economic Theory 3, 373–413.

Nelson, C. R. and A. F. Siegel (1987). Parsimonious Modeling of Yield Curve. Journal of Business 60, 473–489.

Pflug, G. C. (2000). Probabilistic Constrained Optimization - Methodology and Applications, Chapter Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk, pp. 272–281. Kluwer Academic Publishers.

Pflug, G. C. (2001). Optimal Scenario Tree Generation for Multiperiod Financial Planning. Mathematical Programming 89(2), 251 – 271.

Rockafellar, R., S. Uryasev, and M. Zabarankin (2006). Generalized Deviations in Risk Analysis. Finance and Stochastics 10(1), 51–74.

Rockafellar, R. T. and S. Uryasev (2000). Optimization of Conditional Value-at-Risk. Journal of Risk 2(3), 21–41.

Rockafellar, R. T. and S. Uryasev (2002). Conditional Value-at-Risk for General Loss Distributions. Journal of Banking & Finance 26(7), 1443–1471.

Samuelson, P. A. (1969). Lifetime Portfolio Selection by Dynamic Stochastic Programming. Review of Economics and Statistics 51, 239–246.

Wachter, J. (2002). Portfolio and Consumption Decisions under Mean-Reverting Returns: An Exact Solution. Journal of Financial and Quantitative Analysis 37 (1), 63–91.