On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures
This review paper discusses advances of statistical inference in modeling extreme observations from multiple sources and heterogeneous populations. The paper starts briefly reviewing classical univariate/multivariate extreme value theory, tail equivalence, and tail (in)dependence. New extreme value...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2020.1856590 |
| _version_ | 1797676991462244352 |
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| author | Zhengjun Zhang |
| author_facet | Zhengjun Zhang |
| author_sort | Zhengjun Zhang |
| collection | DOAJ |
| description | This review paper discusses advances of statistical inference in modeling extreme observations from multiple sources and heterogeneous populations. The paper starts briefly reviewing classical univariate/multivariate extreme value theory, tail equivalence, and tail (in)dependence. New extreme value theory for heterogeneous populations is then introduced. Time series models for maxima and extreme observations are the focus of the review. These models naturally form a new system with similar structures. They can be used as alternatives to the widely used ARMA models and GARCH models. Applications of these time series models can be in many fields. The paper discusses two important applications: systematic risks and extreme co-movements/large scale contagions. |
| first_indexed | 2024-03-11T22:38:29Z |
| format | Article |
| id | doaj.art-07f5c3f4343c4196ba5ffc50694044ae |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:38:29Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-07f5c3f4343c4196ba5ffc50694044ae2023-09-22T09:19:46ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772021-01-015112510.1080/24754269.2020.18565901856590On studying extreme values and systematic risks with nonlinear time series models and tail dependence measuresZhengjun Zhang0University of Wisconsin-MadisonThis review paper discusses advances of statistical inference in modeling extreme observations from multiple sources and heterogeneous populations. The paper starts briefly reviewing classical univariate/multivariate extreme value theory, tail equivalence, and tail (in)dependence. New extreme value theory for heterogeneous populations is then introduced. Time series models for maxima and extreme observations are the focus of the review. These models naturally form a new system with similar structures. They can be used as alternatives to the widely used ARMA models and GARCH models. Applications of these time series models can be in many fields. The paper discusses two important applications: systematic risks and extreme co-movements/large scale contagions.http://dx.doi.org/10.1080/24754269.2020.1856590extreme value theorytail dependence indextime series of maximamaxima of moving maximaautoregressive tail index models |
| spellingShingle | Zhengjun Zhang On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures Statistical Theory and Related Fields extreme value theory tail dependence index time series of maxima maxima of moving maxima autoregressive tail index models |
| title | On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| title_full | On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| title_fullStr | On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| title_full_unstemmed | On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| title_short | On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| title_sort | on studying extreme values and systematic risks with nonlinear time series models and tail dependence measures |
| topic | extreme value theory tail dependence index time series of maxima maxima of moving maxima autoregressive tail index models |
| url | http://dx.doi.org/10.1080/24754269.2020.1856590 |
| work_keys_str_mv | AT zhengjunzhang onstudyingextremevaluesandsystematicriskswithnonlineartimeseriesmodelsandtaildependencemeasures |