{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Estimation and Detection of Network Variation in Intraday Stock Market","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/11401/77188"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"The last few years have witnessed an exponential growth in the collection and analysis of financial market data. Investigating the interactions between the dynamics of the financial system and extracting useful information from these multivariate data streams can help us in improving our understanding of the underlying backbone in the financial market. These massive noisy data sets require the application of suitable and efficient dependency measurements for their analysis in a real-time environment. And that is why network analysis has emerged recently, which is a plausible representation helps interpret the hidden interconnection between the elements in large datasets. However, most frequently used methods in this area have certain limitations, such as the computational complexity or the assumption of a temporally invariant network. This thesis has two major purposes: firstly, to construct time-varying networks by presenting two new approaches to dynamically measure symmetric and asymmetric interactions; and secondly, to detect the structural breaks in the high dimensional time series of the financial market. Building on previous work, we propose two computationally efficient approaches based on partial correlation network and vector autoregressive adjacency network. Since both of these estimators are under the high-dimension-low-sample-size setting, we develop a penalized kernel smoothing method for the problem of selecting non-zero elements of the time-varying matrix. The network structures of multivariate financial time series are established for the first time for such estimators and displayed in a graphical representation. Furthermore, we consider the problem of efficient financial surveillance aimed at prompt detection of structural breaks in the market. Assuming the model evolves in a piece-wise constant fashion, we study four types of detection rules, including statistical process control chart, generalized likelihood ratio detection rule, a detection method based on an extension of Shiryaev's Bayesian single change point model and a sequential detection rule for multiple change points. The efficiency of the proposed methods is demonstrated on both simulation studies and the empirical analysis focusing primarily on the intraday stock market. Our findings shed a new light on uncovering the hidden interactions between the financial dynamics and present new insight into market structure and market stability."},{"label":"dcterms.available","value":"2017-09-20T16:52:10Z"},{"label":"dcterms.contributor","value":"Xiao, Keli."},{"label":"dcterms.creator","value":"Li, Shanshan"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:52:10Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:52:10Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics"},{"label":"dcterms.extent","value":"157 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77188"},{"label":"dcterms.issued","value":"2016-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:52:10Z (GMT). No. of bitstreams: 1\nLi_grad.sunysb_0771E_13099.pdf: 18155838 bytes, checksum: 51f42d579d10462bdb72e42e53a25ff9 (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Applied mathematics -- Statistics -- Finance"},{"label":"dcterms.title","value":"Estimation and Detection of Network Variation in Intraday Stock Market"},{"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%2F97%2F03%2F38970360600295179102199060583580441756/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%2F97%2F03%2F38970360600295179102199060583580441756","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}