{"title":"价格过程跳跃强度的长记忆","authors":"Yizhuang Tian, Dongyang Shi, Handong Li","doi":"10.4236/JMF.2021.112009","DOIUrl":null,"url":null,"abstract":"The impact of successive jumps in price process \non volatility is very important. We study the nature of self-motivation in \nprice process using data from China’s stock market. Our empirical results \nsuggest that: 1) Price jumps in China’s stock market are generally \nself-motivated, i.e., price jumps are clustering. 2) The jump intensity of \nChina’s stock market is time-varying, and follows log-normal distribution, \nwhich indicates that the jump intensity is asymmetrical. 3) The jump \nintensities’ sequence exhibits typical long memory.","PeriodicalId":299894,"journal":{"name":"Journal of Mathematical Finance","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Long Memory of the Jump Intensity of the Price Process\",\"authors\":\"Yizhuang Tian, Dongyang Shi, Handong Li\",\"doi\":\"10.4236/JMF.2021.112009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The impact of successive jumps in price process \\non volatility is very important. We study the nature of self-motivation in \\nprice process using data from China’s stock market. Our empirical results \\nsuggest that: 1) Price jumps in China’s stock market are generally \\nself-motivated, i.e., price jumps are clustering. 2) The jump intensity of \\nChina’s stock market is time-varying, and follows log-normal distribution, \\nwhich indicates that the jump intensity is asymmetrical. 3) The jump \\nintensities’ sequence exhibits typical long memory.\",\"PeriodicalId\":299894,\"journal\":{\"name\":\"Journal of Mathematical Finance\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mathematical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/JMF.2021.112009\",\"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 Mathematical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/JMF.2021.112009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Long Memory of the Jump Intensity of the Price Process
The impact of successive jumps in price process
on volatility is very important. We study the nature of self-motivation in
price process using data from China’s stock market. Our empirical results
suggest that: 1) Price jumps in China’s stock market are generally
self-motivated, i.e., price jumps are clustering. 2) The jump intensity of
China’s stock market is time-varying, and follows log-normal distribution,
which indicates that the jump intensity is asymmetrical. 3) The jump
intensities’ sequence exhibits typical long memory.