Tail Risk Analysis for Financial Time Series
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913521163304960 |
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| author | Kiriliouk, Anna Zhou, Chen |
| author_facet | Kiriliouk, Anna Zhou, Chen |
| contents | This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimation, unconditional and conditional quantile forecasting. We use the S&P 500 index as a case study to assess serial (extremal) dependence, perform an unconditional and conditional risk analysis, and apply backtesting methods. Additionally, the chapter explores the impact of serial dependence on multivariate tail dependence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tail Risk Analysis for Financial Time Series Kiriliouk, Anna Zhou, Chen Risk Management Applications Methodology This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimation, unconditional and conditional quantile forecasting. We use the S&P 500 index as a case study to assess serial (extremal) dependence, perform an unconditional and conditional risk analysis, and apply backtesting methods. Additionally, the chapter explores the impact of serial dependence on multivariate tail dependence. |
| title | Tail Risk Analysis for Financial Time Series |
| topic | Risk Management Applications Methodology |
| url | https://arxiv.org/abs/2409.18643 |