Massachusetts Institute of Technology - Sloan School of Management
Computational Finance Seminar
All individuals and institutions regularly face asset liability decision making. I discuss an approach to model such decisions for pension funds, insurance companies, individuals, retirees, bank trading departments, and hedge funds. It includes uncertainties, constraints, risks, transactions costs, liquidity, and preferences over time, to provide good results in normal times and avoid or limit disaster when extreme scenarios occur. The stochastic programming approach while complex is a practical way to include key problem elements that other approaches are not able to model. Other approaches (static mean variance, fixed mix, stochastic control, capital growth, continuous time finance etc.) are useful for the micro analysis of decisions and the SP approach is useful for the aggregated macro (overall) analysis of relevant decisions and activities. Other approaches yield good results most of the time but they frequently lead to the recipe for disaster: over-betting and not being actually diversified at a time when an extreme scenario occurs; and they are awkward or impossible to employ with liabilities, market imperfections and policy and legal constraints. With derivative trading, positions are changing constantly and a non-overbet situation can become an overbet quickly. Uncertainties are modeled using discrete probability scenarios that approximate the true probability distributions. The accuracy of the scenarios chosen and their probabilities contribute greatly to model success. However, the scenario approach generally leads to superior investment performance even if there are errors in the estimations of both the actual scenario outcomes and their probabilities. The modeling effort attempts to cover well the range of possible future evolution of the economic environment, and that should yield good results and avoid disasters. Worldwide Asset Liability Management (CUP, 1998), The Stochastic Programming Approach to Asset Liability Management (AIMR 2003) and the Siemens Austria Pension Fund model are references. The predominant view is that such models do not exist, are impossible to successfully implement, or that they are prohibitively expensive. Given modern computer power, better basic large scale stochastic linear programming codes, and better modeling skills, such models can be widely used in many applications and are very cost effective.
* Dr William T. Ziemba is the Alumni Professor of Financial Modeling and Stochastic Optimization, Emeritus , Sauder School of Business, University of British Columbia, Vancouver and Visiting Professor of Finance, Sloan School of Management, MIT, Cambridge, MA. His PhD is from the University of California, Berkeley. He has been a visiting professor at Stanford, UCLA, Berkeley, and Chicago and a number of universities in Europe and Asia. He has been a consultant to the Frank Russell Company. His research is in asset-liability management, portfolio theory and practice, security market imperfections, Japanese and Asian financial markets, sports and lottery investments and applied stochastic programming.