DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chun, Dohyun | ko |
dc.contributor.author | Kim, Donggyu | ko |
dc.date.accessioned | 2021-12-23T06:40:29Z | - |
dc.date.available | 2021-12-23T06:40:29Z | - |
dc.date.created | 2021-07-19 | - |
dc.date.created | 2021-07-19 | - |
dc.date.created | 2021-07-19 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | JOURNAL OF TIME SERIES ANALYSIS, v.43, no.1, pp.105 - 124 | - |
dc.identifier.issn | 0143-9782 | - |
dc.identifier.uri | http://hdl.handle.net/10203/290960 | - |
dc.description.abstract | Recently, to account for low-frequency market dynamics, several volatility models, employing high-frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous structure. In this article, to study state heterogeneous market dynamics based on high-frequency data, we introduce a novel volatility model based on a continuous Ito diffusion process whose intraday instantaneous volatility process evolves depending on the exogenous state variable, as well as its integrated volatility. We call it the state heterogeneous GARCH-Ito (SG-Ito) model. We suggest a quasi-likelihood estimation procedure with the realized volatility proxy and establish its asymptotic behaviors. Moreover, to test the low-frequency state heterogeneity, we develop a Wald test-type hypothesis testing procedure. The results of empirical studies suggest the existence of leverage, investor attention, market illiquidity, stock market comovement, and post-holiday effect in S&P 500 index volatility. | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | State Heterogeneity Analysis of Financial Volatility using high-frequency Financial Data | - |
dc.type | Article | - |
dc.identifier.wosid | 000670037700001 | - |
dc.identifier.scopusid | 2-s2.0-85109141545 | - |
dc.type.rims | ART | - |
dc.citation.volume | 43 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 105 | - |
dc.citation.endingpage | 124 | - |
dc.citation.publicationname | JOURNAL OF TIME SERIES ANALYSIS | - |
dc.identifier.doi | 10.1111/jtsa.12594 | - |
dc.contributor.localauthor | Kim, Donggyu | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | GARCH | - |
dc.subject.keywordAuthor | diffusion process | - |
dc.subject.keywordAuthor | regime switching | - |
dc.subject.keywordAuthor | quasi-maximum likelihood estimator | - |
dc.subject.keywordAuthor | Wald test | - |
dc.subject.keywordPlus | STOCK RETURNS | - |
dc.subject.keywordPlus | TRADING VOLUME | - |
dc.subject.keywordPlus | CONDITIONAL HETEROSCEDASTICITY | - |
dc.subject.keywordPlus | STOCHASTIC VOLATILITY | - |
dc.subject.keywordPlus | OVERNIGHT INFORMATION | - |
dc.subject.keywordPlus | MATRIX ESTIMATION | - |
dc.subject.keywordPlus | ASYMPTOTIC THEORY | - |
dc.subject.keywordPlus | GARCH MODELS | - |
dc.subject.keywordPlus | LIKELIHOOD | - |
dc.subject.keywordPlus | LEVERAGE | - |
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