{"title":"风险周期值","authors":"V. Khokhlov","doi":"10.2139/ssrn.1976213","DOIUrl":null,"url":null,"abstract":"Many practitioners annualize VaR just like the standard deviation. We show that this approach is incorrect, and a more sophisticated formula should be used for deriving a periodic VaR from parameters of the daily returns distribution. Another problem addressed here is the distribution of daily and periodic returns and its effect on VaR. While a fat-tailed distribution is more appropriate for modeling daily returns, we show that using the log-normal distribution is still a reasonable choice for modeling periodic returns and calculating a periodic VaR for holding periods of one month and longer.","PeriodicalId":11800,"journal":{"name":"ERN: Stock Market Risk (Topic)","volume":"87 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2011-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Periodic Value at Risk\",\"authors\":\"V. Khokhlov\",\"doi\":\"10.2139/ssrn.1976213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many practitioners annualize VaR just like the standard deviation. We show that this approach is incorrect, and a more sophisticated formula should be used for deriving a periodic VaR from parameters of the daily returns distribution. Another problem addressed here is the distribution of daily and periodic returns and its effect on VaR. While a fat-tailed distribution is more appropriate for modeling daily returns, we show that using the log-normal distribution is still a reasonable choice for modeling periodic returns and calculating a periodic VaR for holding periods of one month and longer.\",\"PeriodicalId\":11800,\"journal\":{\"name\":\"ERN: Stock Market Risk (Topic)\",\"volume\":\"87 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Stock Market Risk (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.1976213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Stock Market Risk (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1976213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many practitioners annualize VaR just like the standard deviation. We show that this approach is incorrect, and a more sophisticated formula should be used for deriving a periodic VaR from parameters of the daily returns distribution. Another problem addressed here is the distribution of daily and periodic returns and its effect on VaR. While a fat-tailed distribution is more appropriate for modeling daily returns, we show that using the log-normal distribution is still a reasonable choice for modeling periodic returns and calculating a periodic VaR for holding periods of one month and longer.