Optimal chance-constrained pension fund management through dynamic stochastic control

Davide Lauria (), Giorgio Consigli () and Francesca Maggioni ()
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Davide Lauria: Texas Tech University
Giorgio Consigli: Khalifa University of Science and Technology
Francesca Maggioni: University of Bergamo

OR Spectrum: Quantitative Approaches in Management, 2022, vol. 44, issue 3, No 10, 967-1007

Abstract: Abstract We apply a dynamic stochastic control (DSC) approach based on an open-loop linear feedback policy to a classical asset-liability management problem as the one faced by a defined-benefit pension fund (PF) manager. We assume a PF manager seeking an optimal investment policy under random market returns and liability costs as well as stochastic PF members’ survival rates. The objective function is formulated as a risk-reward trade-off function resulting in a quadratic programming problem. The proposed methodology combines a stochastic control approach, due to Primbs and Sung (IEEE Trans Autom Control 54(2):221–230, 2009), with a chance constraint on the PF funding ratio (FR) and it is applied for the first time to this class of long-term financial planning problems characterized by stochastic asset and liabilities. Thanks to the DSC formulation, we preserve the underlying risk factors continuous distributions and avoid any state space discretization as is typically the case in multistage stochastic programs (MSP). By distinguishing between a long-term PF liability projection horizon and a shorter investment horizon for the FR control, we avoid the curse-of-dimensionality, in-sample instability and approximation errors that typically characterize MSP formulations. Through an extended computational study, we present in- and out-of-sample results which allows us to validate the proposed methodology. The collected evidences confirm the potential of this approach when applied to a stylized but sufficiently realistic long-term PF problem.

Keywords: Stochastic control; Chance constraints; Pension funds; Asset-liability management (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00291-022-00673-0

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