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Title: Integrated Asset Allocation Strategies: Application to Institutional Investors
Authors: Lin, Changle
Advisors: Mulvey, John
Contributors: Operations Research and Financial Engineering Department
Keywords: asset allocation model
financial engineering
machine learning
real option
stochastic control
Subjects: Operations research
Issue Date: 2016
Publisher: Princeton, NJ : Princeton University
Abstract: Investors with incomes from businesses need to make investment decisions in face of business decisions. Prominent examples include: sovereign wealth funds with state businesses, pension funds with sponsor companies, family offices with family businesses, households with salary incomes, etc. This paper establishes a general theoretical framework to analyze optimal investment decisions for these entities. The machinery for solving the integrated asset allocation problem is developed. And the theoretical proof is given. We also develop applications to oil-based sovereign wealth funds and family offices to illustrate the usage of the framework. Oil-based sovereign wealth funds (SWF) are set up by oil-producing countries. The SWF gets transfers from oil business incomes. Examples include Norway's Government Pension Fund Global (GPFG) and Abu Dhabi Investment Authority (ADIA). Family offices are set up by wealthy families. The family office generally gets transfers from family business incomes. Applications to SWFs and family offices involve two elements: first, the asset allocation problem is modeled according to the general framework in a stochastic control setting; second, we invent a real option pricing technique to value all future claims on business incomes. The integrated optimal asset allocation is then solved with both elements. The superiority of integrated optimal decisions are demonstrated via these applications.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog:
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Operations Research and Financial Engineering

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