DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Donggyu | ko |
dc.contributor.author | Oh, Minseog | ko |
dc.contributor.author | Wang, Yazhen | ko |
dc.date.accessioned | 2022-06-07T07:01:08Z | - |
dc.date.available | 2022-06-07T07:01:08Z | - |
dc.date.created | 2021-12-07 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.citation | JOURNAL OF TIME SERIES ANALYSIS, v.43, no.4, pp.640 - 665 | - |
dc.identifier.issn | 0143-9782 | - |
dc.identifier.uri | http://hdl.handle.net/10203/296847 | - |
dc.description.abstract | This article introduces a novel quantile approach to harness the high-frequency information and improve the daily conditional quantile estimation. Specifically, we model the conditional standard deviation as a realized generalized autoregressive conditional heteroskedasticity (GARCH) model and employ conditional standard deviation, realized volatility, realized quantile, and absolute overnight return as innovations in the proposed dynamic quantile models. We devise a two-step estimation procedure to estimate the conditional quantile parameters. The first step applies a quasi-maximum likelihood estimation procedure, with the realized volatility as a proxy for the volatility proxy, to estimate the conditional standard deviation parameters. The second step utilizes a quantile regression estimation procedure with the estimated conditional standard deviation in the first step. Asymptotic theory is established for the proposed estimation methods, and a simulation study is conducted to check their finite-sample performance. Finally, we apply the proposed methodology to calculate the value at risk of 20 individual assets and compare its performance with existing competitors. | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | Conditional quantile analysis for realized GARCH models | - |
dc.type | Article | - |
dc.identifier.wosid | 000723440700001 | - |
dc.identifier.scopusid | 2-s2.0-85120040467 | - |
dc.type.rims | ART | - |
dc.citation.volume | 43 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 640 | - |
dc.citation.endingpage | 665 | - |
dc.citation.publicationname | JOURNAL OF TIME SERIES ANALYSIS | - |
dc.identifier.doi | 10.1111/jtsa.12633 | - |
dc.contributor.localauthor | Kim, Donggyu | - |
dc.contributor.nonIdAuthor | Wang, Yazhen | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | High-frequency financial data | - |
dc.subject.keywordAuthor | quasi-maximum likelihood estimation | - |
dc.subject.keywordAuthor | realized volatility | - |
dc.subject.keywordAuthor | risk management | - |
dc.subject.keywordAuthor | value at risk | - |
dc.subject.keywordPlus | VOLATILITY | - |
dc.subject.keywordPlus | FREQUENCY | - |
dc.subject.keywordPlus | MATRIX | - |
dc.subject.keywordPlus | HETEROSCEDASTICITY | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordPlus | RISK | - |
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