{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Log Band Fraction Approximation For Covariance Estimation and Low Volatility Strategy","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77488"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"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."},{"label":"dcterms.available","value":"2017-09-20T16:52:48Z"},{"label":"dcterms.contributor","value":"Xing, Haipeng"},{"label":"dcterms.creator","value":"Yu, Riyu"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:48Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:48Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"93 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77488"},{"label":"dcterms.issued","value":"2015-05-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:48Z (GMT). No. of bitstreams: 1\nYu_grad.sunysb_0771E_12498.pdf: 628211 bytes, checksum: 1e5ab5e7aa200b2f4a40022c83660275 (MD5)\n  Previous issue date: 2015"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Applied mathematics"},{"label":"dcterms.title","value":"Log Band Fraction Approximation For Covariance Estimation and Low Volatility Strategy"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/72%2F96%2F22%2F72962226657683039343919303877803039164/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/72%2F96%2F22%2F72962226657683039343919303877803039164","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}