{"title":"基于LSTM-EGARCH组合模型的VaR碳交易风险度量研究","authors":"文国 蒋","doi":"10.12677/wjf.2023.124030","DOIUrl":null,"url":null,"abstract":"Based on the information of carbon trading price returns, this study investigates the risk of the carbon trading market system from the perspective of deep learning theory. A LSTM-EGARCH volatility dynamic prediction model is constructed, and EVT semi-parametric method, as well as","PeriodicalId":71691,"journal":{"name":"林业世界","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on VaR Carbon Trading Risk Measurement Based on LSTM-EGARCH Combined Modeling\",\"authors\":\"文国 蒋\",\"doi\":\"10.12677/wjf.2023.124030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the information of carbon trading price returns, this study investigates the risk of the carbon trading market system from the perspective of deep learning theory. A LSTM-EGARCH volatility dynamic prediction model is constructed, and EVT semi-parametric method, as well as\",\"PeriodicalId\":71691,\"journal\":{\"name\":\"林业世界\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"林业世界\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12677/wjf.2023.124030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"林业世界","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12677/wjf.2023.124030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on VaR Carbon Trading Risk Measurement Based on LSTM-EGARCH Combined Modeling
Based on the information of carbon trading price returns, this study investigates the risk of the carbon trading market system from the perspective of deep learning theory. A LSTM-EGARCH volatility dynamic prediction model is constructed, and EVT semi-parametric method, as well as
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