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Log Band Fraction Approximation For Covariance Estimation and Low Volatility Strategy

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dc.contributor.advisor Mullhaupt, Andrew P en_US
dc.contributor.author Yu, Riyu en_US
dc.contributor.other Department of Applied Mathematics and Statistics en_US
dc.date.accessioned 2017-09-20T16:52:48Z
dc.date.available 2017-09-20T16:52:48Z
dc.date.issued 2015-05-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77488 en_US
dc.description 93 pgs en_US
dc.description.abstract Structured matrix plays an important role in statistics, especially in covariance estimation. Band fraction representation is one of the efficient structures for matrices. In this dissertation, we study the metric tensor for the band fraction representation for the covariance matrix. We propose a new structure, the log band fraction representation, which gives smaller information distance and Hellinger distance than factor model and band fraction representation. We apply the log band fraction estimation in the portfolio optimization problem. We propose our long only strategy and 130-30 strategy, which significantly outperform the benchmarks, i.e., SPY, SPLV, and CSM. Transaction cost is considered in the portfolio construction process. The strategies proposed in this dissertation are fully investable. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Applied mathematics en_US
dc.subject.other 130-30 Fund, Fisher Information, Log Band Fraction, Long Only Portfolio, Low Volatility Strategy en_US
dc.title Log Band Fraction Approximation For Covariance Estimation and Low Volatility Strategy en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Rachev, Svetlozar en_US
dc.contributor.committeemember Xing, Haipeng en_US
dc.contributor.committeemember Xiao, Keli en_US

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