{"title":"基于高频数据的流动性调整VaR计量:模型构建与回验","authors":"Xiao-xing LIU","doi":"10.1016/S1874-8651(10)60057-9","DOIUrl":null,"url":null,"abstract":"<div><p>This article constructed a WACD(1,1)-UHF-GARCH(1,1)-IVaR Model to the stock market of China with the theory of ACD and UHF-GARCH, and took Shanghai Pudong Development Bank as an example, gave an empirical analysis to the intraday Value at Risk adjusted by liquidity in our stock market combining the liquidity indicators designed by price impact model. The result shows that: first, the transactions duration has strong clustering character; second, there is also strong GARCH effect on high-frequency data, and good news will come out more volatility than bad news, but the effect of both news impacting on the market have obviously reduced after considering the influence of liquidity; finally, Monte Carlo simulation shows that the Value at Risk will underestimate the real losses without considering the liquidity effects.</p></div>","PeriodicalId":101206,"journal":{"name":"Systems Engineering - Theory & Practice","volume":"29 7","pages":"Pages 16-26"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1874-8651(10)60057-9","citationCount":"5","resultStr":"{\"title\":\"Liquidity-Adjusted VaR Measurement based on High-Frequency Data: Model Constructing and Backtest\",\"authors\":\"Xiao-xing LIU\",\"doi\":\"10.1016/S1874-8651(10)60057-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This article constructed a WACD(1,1)-UHF-GARCH(1,1)-IVaR Model to the stock market of China with the theory of ACD and UHF-GARCH, and took Shanghai Pudong Development Bank as an example, gave an empirical analysis to the intraday Value at Risk adjusted by liquidity in our stock market combining the liquidity indicators designed by price impact model. The result shows that: first, the transactions duration has strong clustering character; second, there is also strong GARCH effect on high-frequency data, and good news will come out more volatility than bad news, but the effect of both news impacting on the market have obviously reduced after considering the influence of liquidity; finally, Monte Carlo simulation shows that the Value at Risk will underestimate the real losses without considering the liquidity effects.</p></div>\",\"PeriodicalId\":101206,\"journal\":{\"name\":\"Systems Engineering - Theory & Practice\",\"volume\":\"29 7\",\"pages\":\"Pages 16-26\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/S1874-8651(10)60057-9\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Engineering - Theory & Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1874865110600579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Engineering - Theory & Practice","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874865110600579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Liquidity-Adjusted VaR Measurement based on High-Frequency Data: Model Constructing and Backtest
This article constructed a WACD(1,1)-UHF-GARCH(1,1)-IVaR Model to the stock market of China with the theory of ACD and UHF-GARCH, and took Shanghai Pudong Development Bank as an example, gave an empirical analysis to the intraday Value at Risk adjusted by liquidity in our stock market combining the liquidity indicators designed by price impact model. The result shows that: first, the transactions duration has strong clustering character; second, there is also strong GARCH effect on high-frequency data, and good news will come out more volatility than bad news, but the effect of both news impacting on the market have obviously reduced after considering the influence of liquidity; finally, Monte Carlo simulation shows that the Value at Risk will underestimate the real losses without considering the liquidity effects.
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