Research on the Application and Optimization of Mathematical Models in Financial Market Risk Management.
This paper applied mathematical models to conduct an in-depth discussion and empirical analysis of financial market risk management. The daily rate of return data on the S&P 500 index, selected through data processing, included data cleaning, return calculation, data standardization, construction of...
| Publicado en: | SHS Web of Conferences Vol. 196; pp. 1 - 7 |
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| Formato: | Artículo |
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EDP Sciences
8/26/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179511940&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179511940 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24165182 FT5R jtl: SHS Web of Conferences issn: 24165182 maglogo: N pubinfo: dt: 8/26/2024 vid: 196 pid: 76090 pub: EDP Sciences artinfo: ui: 179511940 10.1051/shsconf/202419603001 ppf: 1 ppct: 6 formats: tig: atl: Research on the Application and Optimization of Mathematical Models in Financial Market Risk Management. aug: au: Pan, Yuyang affil: University College London, UK sug: ab: This paper applied mathematical models to conduct an in-depth discussion and empirical analysis of financial market risk management. The daily rate of return data on the S&P 500 index, selected through data processing, included data cleaning, return calculation, data standardization, construction of the GARCH (1, 1) model, and a Copula model for predicting and analyzing risks. Empirical results indicate that the GARCH model has good simulation in capturing the market volatility change, while the Copula model holds clear advantages in modeling multivariate risk dependencies. In the modern economy, managing risks in the financial market plays a vital role. Optimization algorithms such as genetic algorithms and Bayesian optimization significantly improve the prediction accuracy and computational efficiency of the model. Compared with traditional historical simulation methods, these models perform better in risk prediction indicators (VaR and ES), proving their practicality and effectiveness in actual risk management. The research results verify that advanced mathematical models and optimization methods have important application value in financial market risk management, providing a scientific decision-making basis for investors and risk managers. pubtype: Conference Proceedings doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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