{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Modeling Intra-day Markets with an application of Risk Management  and Optimal Order Execution","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/11401/77532"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"Financial time series data exhibits heavy tailed, volatility clustering and long range dependence style facts. Traditional Gaussian distribution assumption based model failed to explain these phenomena. A unified framework model proposed in this thesis, fractionally integrated autoregressive moving average (FARIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) with multivariate generalized hyperbolic distribution (MGHD), trying to capture all these phenomena together. We also examined this model by using intra-day market dataset to backtest of various risk measure. With rise of high frequency trading and algorithm trading in recent years, trading volume hugely increased and markets became more volatile. Order execution is the main concern for traders, especially in the case of liquidation of big orders. We illustrate how the optimal order execution strategy behaves under the assumption that market price dynamics follows high volatile (non-Gaussian) markets with volatility clustering and log-range dependence characteristics."},{"label":"dcterms.available","value":"2017-09-20T16:52:52Z"},{"label":"dcterms.contributor","value":"Xiao, Keli."},{"label":"dcterms.creator","value":"Chai, Yikang"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:52Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:52Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"82 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77532"},{"label":"dcterms.issued","value":"2014-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:52Z (GMT). No. of bitstreams: 1\nChai_grad.sunysb_0771E_11797.pdf: 3422589 bytes, checksum: b7e5f2cede536cf88e5c8f3c0b9a250e (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"generalized hyperbolic distribution, heavy tails, long memory, optimal order execution"},{"label":"dcterms.title","value":"Modeling Intra-day Markets with an application of Risk Management  and Optimal Order Execution"},{"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/2/38%2F22%2F14%2F38221404771485075798125091641127908339/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/2/38%2F22%2F14%2F38221404771485075798125091641127908339","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}