{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"The Chinese Equity Risk Factor Model Based on Heavy Tailed Distributions, and Its Application in Risk Management and Portfolio Optimization","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/11401/76238"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"In financial risk and portfolio management, the heavy tailed distributions of one-dimensional asset returns and complex dependence structure among multi-dimensional asset returns are two widely discussed problems. Various distributions, which share the desired properties, such as heavy tails, skewness and kurtosis, have been introduced and applied into practice. On the other hand, factor modeling as a technique of dimension reduction, has been used in multivariate statistical analysis and econometric model building, especially in high-dimensional world of financial risk management In this dissertation, we study these two popular problems in risk and portfolio management. The findings are empirically examined through their applications to the Chinese stock markets. More specifically, we study the behavior of stock returns in the Chinese market from 2002 to 2012 and build the advanced risk factor model for risk and portfolio management. Firstly we give an empirical examination of the Chinese market with testing the Gaussian hypothesis and alternative non-Gaussian distribution hypotheses under the (1) unconditional homoscedastic distribution assumption and (2) conditional heteroscedastic distribution assumption. An ARMA-GARCH model with non-Gaussian distributed innovations is applied to the index and backtested during highly volatile market periods from 2006 to 2012. The model provides a strong capacity of forecasting and possible warning signals of coming market crash. Secondly, we build the equity multi-factor model covering the entire Chinese stock markets. Risk factor returns, including market factors and fundamental factors, are estimated though time series regression and cross-sectional regression. The forecasting methods are created with multivariate ARMA-GARCH models for different distributed innovations, and compared with industrial standard methods. Thirdly, we applied the risk factor models for portfolio and risk management, including risk monitoring, risk budgeting and portfolio optimization. Different risk measures and optimization strategies are tested to provide the most suitable tools."},{"label":"dcterms.available","value":"2017-09-20T16:49:46Z"},{"label":"dcterms.contributor","value":"Rachev, Svetlozar T"},{"label":"dcterms.creator","value":"Lu, Tianyu"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:49:46Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:49:46Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"129 pg."},{"label":"dcterms.format","value":"Application/PDF"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/76238"},{"label":"dcterms.issued","value":"2013-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:49:46Z (GMT). No. of bitstreams: 1\nLu_grad.sunysb_0771E_11591.pdf: 3068682 bytes, checksum: b9a71119ffad3e936216994c479702f8 (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Chinese Equity Market, Factor model, Heavy tailed distributions, Portfolio Optimization, Risk management, Time series Analysis"},{"label":"dcterms.title","value":"The Chinese Equity Risk Factor Model Based on Heavy Tailed Distributions, and Its Application in Risk Management and Portfolio Optimization"},{"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/2/12%2F07%2F39%2F120739242165912141676902615615509948844/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/2/12%2F07%2F39%2F120739242165912141676902615615509948844","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}