{"title":"股票的信息负荷","authors":"V. Polimenis","doi":"10.4172/2168-9458.1000114","DOIUrl":null,"url":null,"abstract":"In this short paper, I selectively review some recent developments related to the idea that jumps in stock prices incorporate the most valuable information, and thus the quantification of a stock’s exposure to jump events is important for financial risk management and portfolio construction. There are two main methodologies of estimating jump betas: a) the more widely used high or ultra high frequency procedures that rely on the asymptotical behavior of elaborate and sophisticated econometric constructs, such as the bi-power variation or local averaging techniques in order to isolate market microstructure noise at high frequencies, and b) very recently a new non-parametric skew-based methodology that does not rely on the use of high frequency data and is thus immune to market microstructure noise.","PeriodicalId":315937,"journal":{"name":"Journal of Stock & Forex Trading","volume":"296 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Informational Loadings of a Stock\",\"authors\":\"V. Polimenis\",\"doi\":\"10.4172/2168-9458.1000114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this short paper, I selectively review some recent developments related to the idea that jumps in stock prices incorporate the most valuable information, and thus the quantification of a stock’s exposure to jump events is important for financial risk management and portfolio construction. There are two main methodologies of estimating jump betas: a) the more widely used high or ultra high frequency procedures that rely on the asymptotical behavior of elaborate and sophisticated econometric constructs, such as the bi-power variation or local averaging techniques in order to isolate market microstructure noise at high frequencies, and b) very recently a new non-parametric skew-based methodology that does not rely on the use of high frequency data and is thus immune to market microstructure noise.\",\"PeriodicalId\":315937,\"journal\":{\"name\":\"Journal of Stock & Forex Trading\",\"volume\":\"296 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Stock & Forex Trading\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4172/2168-9458.1000114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Stock & Forex Trading","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2168-9458.1000114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this short paper, I selectively review some recent developments related to the idea that jumps in stock prices incorporate the most valuable information, and thus the quantification of a stock’s exposure to jump events is important for financial risk management and portfolio construction. There are two main methodologies of estimating jump betas: a) the more widely used high or ultra high frequency procedures that rely on the asymptotical behavior of elaborate and sophisticated econometric constructs, such as the bi-power variation or local averaging techniques in order to isolate market microstructure noise at high frequencies, and b) very recently a new non-parametric skew-based methodology that does not rely on the use of high frequency data and is thus immune to market microstructure noise.