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Title: Synthetic Diversification, Smart Randomization, and Commodity Indexing
Authors: Goer, Maximilian Andreas Hubertus
Advisors: Mulvey, John M
Contributors: Operations Research and Financial Engineering Department
Keywords: Asset allocation
Hidden Markov model
Machine learning
Rebalancing gains
Subjects: Finance
Operations research
Issue Date: 2015
Publisher: Princeton, NJ : Princeton University
Abstract: This thesis investigates the use of randomization in asset allocation, and introduces a dynamic commodity index. Randomizing asset holdings can lead to extra rebalancing gains, and lower inter-asset correlations. However, the gains are insignificant in practice. Momentum- and correlation-based smart randomization strategies can improve the performance and provide a promising basis for future research. The second part of this thesis introduces a regime-based dynamically weighted commodity index. In this index, the commodity weights are determined by an optimization model that employs an underlying hidden Markov model. Including regimes in the allocation decision leads to vast improvements in index performance. Several extensions of the regime-based allocation model are introduced in the last chapter of this thesis.
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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