Asset-liability management under time-varying investment opportunities
Stochastic linear programming is a suitable numerical approach for solving practical asset-liability management problems. In this paper, we consider a multi-stage setting under time-varying investment opportunities and propose a decomposition of the benefits in dynamic re-allocation and predictability effects. We use a first-order unrestricted vector autoregressive process to model asset returns and state variables and include, in addition to equity returns and dividend-price ratios, Nelson/Siegel parameters to account for the evolution of the yield curve. The objective is to minimize the Conditional Value at Risk of shareholder value, i.e., the difference between the mark-to-market value of (financial) assets and the present value of future liabilities.
Year of publication: |
2011
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Authors: | Ferstl, Robert ; Weissensteiner, Alex |
Published in: |
Journal of Banking & Finance. - Elsevier, ISSN 0378-4266. - Vol. 35.2011, 1, p. 182-192
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Publisher: |
Elsevier |
Keywords: | Asset-liability management Predictability Stochastic programming Scenario generation VAR process |
Saved in:
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