The use of simulation and multi-stage stochastic programming in the optimization of fixed-income investment portfolios is considered. We provide a thorough overview of the problem, discussing what are considered to be state-of-the-art approaches in both industry and in academia, and clearly establish our motivation to depart from these standards. An introduction to dynamic term-structure models, highlighting the features of several specific models that we find are the most pertinent to our task, and an outline of the methods we implement to produce simulations are provided. These are used to build a scenario tree that adequately encapsulates the potential movements of a large set of U.S. Treasury products. The selection of included products, the topology of the tree, and the evolution of uninvested cash are discussed in detail. Particular attention is given to the pricing of coupon bonds as they are auctioned off in the marketplace, and to the computational challenges introduced by our multi-stage approach. We then cast our decision problem into a stochastic programming framework, and compare this approach to the methods typically implemented in the industry. The specification of investor goals is discussed and used to demonstrate the versatility of our approach. The computational expense of our methodology is analyzed, and we note how the use of variance reduction and decomposition methods can reduce this expense. Lastly we explore the use of a downside risk measure and use it to assess the performance of our model and to directly demonstrate how our model outperforms strategies based on serially linked single stage solutions. The analysis includes the formulation of upper and lower bounds, a sophisticated out-of-sample testing procedure to assess the adequacy of our scenario tree size, and a novel evaluation of the robustness of our method with respect to model misspecification.
|School Location:||United States -- California|
|Source:||DAI-B 70/01, Dissertation Abstracts International|
|Subjects:||Finance, Operations research|
|Keywords:||Fixed-income, Portfolio construction, Stochastic programming|
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