The Innovest Austrian Pension Fund Financial Planning Model InnoALM: OR PRACTICE

Alois Geyer and William T Ziemba

Chapter 12 in Selected Works of William T Ziemba:A Memorial Volume, 2024, pp 201-214 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This paper describes the financial planning model InnoALM we developed at Innovest for the Austrian pension fund of the electronics firm Siemens. The model uses a multiperiod stochastic linear programming framework with a flexible number of time periods of varying length. Uncertainty is modeled using multiperiod discrete probability scenarios for random return and other model parameters. The correlations across asset classes, of bonds, stocks, cash, and other financial instruments, are state dependent using multiple correlation matrices that correspond to differing market conditions. This feature allows InnoALM to anticipate and react to severe as well as normal market conditions. Austrian pension law and policy considerations can be modeled as constraints in the optimization. The concave risk-averse preference function is to maximize the expected present value of terminal wealth at the specified horizon net of expected discounted convex (piecewise-linear) penalty costs for wealth and benchmark targets in each decision period. InnoALM has a user interface that provides visualization of key model outputs, the effect of input changes, growing pension benefits from increased deterministic wealth target violations, stochastic benchmark targets, security reserves, policy changes, etc. The solution process using the IBM OSL stochastic programming code is fast enough to generate virtually online decisions and results and allows for easy interaction of the user with the model to improve pension fund performance. The model has been used since 2000 for Siemens Austria, Siemens worldwide, and to evaluate possible pension fund regulation changes in Austria.

Keywords: William Ziemba; Financial Planning Models; Racetrack Betting; Sports Analytics; Market Anomalies; Risk Factors (search for similar items in EconPapers)
JEL-codes: C44 C6 G11 G12 (search for similar items in EconPapers)
Date: 2024
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