Extreme Value Theory for Time Series Models with Power-Law Tails /
This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models. Rigorous descriptions o...
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| Автори: | , |
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| Співавтор: | |
| Формат: | Електронний ресурс eКнига |
| Мова: | Англійська |
| Опубліковано: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Редагування: | 1st ed. 2024. |
| Серія: | Springer Series in Operations Research and Financial Engineering,
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| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/978-3-031-59156-3 |
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Зміст:
- Introduction
- Part 1 Regular variation of distributions and processes
- 2 The iid univariate benchmark
- 3 Regularly varying random variables and vectors
- 4 Regularly varying time series
- 5 Examples of regularly varying stationary processes
- Part 2 Point process convergence and cluster phenomena of time series
- 6 Clusters of extremes
- 7 Point process convergence for regularly varying sequences
- 8 Applications of point process convergence
- Part 3 Infinite variance central limit theory
- 9 Infinite-variance central limit theory
- 10 Self-normalization, sample autocorrelations and the extremogram
- Appendix A Point processes
- Appendix B Univariate regular variation
- Appendix C Vague convergence
- Appendix D Tools
- Appendix E Multivariate regular variation – supplementary results
- Appendix F Heavy-tail large deviations for sequences of independent random variables and vectors, and their applications.-references
- index
- List of abbreviations and symbols.