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- Publisher Website: 10.1111/jtsa.12464
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Article: Inference for asymmetric exponentially weighted moving average models
Title | Inference for asymmetric exponentially weighted moving average models |
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Authors | |
Keywords | Asymmetric EWMA model maximum likelihood estimation non‐stationarity volatility model |
Issue Date | 2020 |
Publisher | Wiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892 |
Citation | Journal of Time Series Analysis, 2020, v. 41 n. 1, p. 154-162 How to Cite? |
Abstract | The exponentially weighted moving average (EWMA) model in ‘Risk‐Metrics’ has been a benchmark for controlling and forecasting risks in financial operations. However, it is incapable of capturing the asymmetric volatility effect and the heavy‐tailed innovation, which are two important stylized features of financial returns. We propose a new asymmetric EWMA model driven by the Student's t‐distributed innovations to take these two stylized features into account and study its maximum likelihood estimation and model diagnostic checking. The finite‐sample performance of the estimation and diagnostic test statistic is examined by the simulated data. |
Persistent Identifier | http://hdl.handle.net/10722/280037 |
ISSN | 2023 Impact Factor: 1.2 2023 SCImago Journal Rankings: 0.875 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, D | - |
dc.contributor.author | Zhu, K | - |
dc.date.accessioned | 2019-12-23T08:25:17Z | - |
dc.date.available | 2019-12-23T08:25:17Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Journal of Time Series Analysis, 2020, v. 41 n. 1, p. 154-162 | - |
dc.identifier.issn | 0143-9782 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280037 | - |
dc.description.abstract | The exponentially weighted moving average (EWMA) model in ‘Risk‐Metrics’ has been a benchmark for controlling and forecasting risks in financial operations. However, it is incapable of capturing the asymmetric volatility effect and the heavy‐tailed innovation, which are two important stylized features of financial returns. We propose a new asymmetric EWMA model driven by the Student's t‐distributed innovations to take these two stylized features into account and study its maximum likelihood estimation and model diagnostic checking. The finite‐sample performance of the estimation and diagnostic test statistic is examined by the simulated data. | - |
dc.language | eng | - |
dc.publisher | Wiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892 | - |
dc.relation.ispartof | Journal of Time Series Analysis | - |
dc.rights | Preprint This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Postprint This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.subject | Asymmetric EWMA model | - |
dc.subject | maximum likelihood estimation | - |
dc.subject | non‐stationarity | - |
dc.subject | volatility model | - |
dc.title | Inference for asymmetric exponentially weighted moving average models | - |
dc.type | Article | - |
dc.identifier.email | Zhu, K: mazhuke@hku.hk | - |
dc.identifier.authority | Zhu, K=rp02199 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/jtsa.12464 | - |
dc.identifier.scopus | eid_2-s2.0-85063378107 | - |
dc.identifier.hkuros | 308834 | - |
dc.identifier.volume | 41 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 154 | - |
dc.identifier.epage | 162 | - |
dc.identifier.isi | WOS:000500797700009 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0143-9782 | - |