Robust Approaches to Pension Fund Asset Liability Management Under Uncertainty
This entry considers the problem of a typical pension fund that collects premiums from sponsors or employees and is liable for fixed payments to its customers after retirement. The fund manager’s goal is to determine an investment strategy so that the fun
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Robust Approaches to Pension Fund Asset Liability Management Under Uncertainty Dessislava Pachamanova, Nalan Gülpınar, and Ethem Çanako˘glu
Abstract This entry considers the problem of a typical pension fund that collects premiums from sponsors or employees and is liable for fixed payments to its customers after retirement. The fund manager’s goal is to determine an investment strategy so that the fund can cover its liabilities while minimizing contributions from its sponsors and maximizing the value of its assets. We develop robust optimization and scenario-based stochastic programming approaches for optimal asset-liability management, taking into consideration the uncertainty in asset returns and future liabilities. Our focus is on computational tractability and ease of implementation under conditions typically encountered in practice, such as asymmetries in the distributions of asset returns. Computational results from tests with real and generated data are presented to illustrate the performance of these models. Keywords Asset-liability management • Uncertainty • Stochastic programming • Robust optimization • Asymmetry
4.1 Introduction Asset-liability management (ALM) is one of the classical problems in financial risk management. Typically, ALM involves the management of assets in such a way as to earn adequate return while maintaining a comfortable surplus of assets over existing and future liabilities. This type of problem is faced by a number of financial services companies, such as pension funds and insurance companies. As
D. Pachamanova () Mathematics and Sciences Division, Babson College, Babson Park, MA 02457, USA e-mail: [email protected] N. Gülpınar Warwick Business School, University of Warwick, Coventry CV4 7AL, UK e-mail: [email protected] E. Çanako˘glu Industrial Engineering, Bahcesehir University, Istanbul, Turkey e-mail: [email protected] © Springer International Publishing Switzerland 2017 G. Consigli et al. (eds.), Optimal Financial Decision Making under Uncertainty, International Series in Operations Research & Management Science 245, DOI 10.1007/978-3-319-41613-7_4
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we will explain in more detail later, the problem of finding optimal ALM policies is computationally challenging, and many of the approaches for implementation described in the literature can be too intensive computationally to implement in practice. There is an extensive literature on modeling and optimization of allocation strategies for ALM based on stochastic programming techniques; see, for example, Ziemba and Mulvey [33]. These approaches usually focus on finding optimal investment rules over a set of scenarios for the future returns on the assets and the liabilities of the company. Such methods have been successfully applied in some instances (see, for example, [29], [15], [16], and [21]); however, they are still not widely used in the financial industry for several reasons. First, ALM is inherently a multiperiod problem, and the number of scenarios needed to
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