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.

翻译：本章阐述如何将极值统计方法应用于金融时间序列数据。此类数据通常呈现强烈的序列依赖性，使得尾部风险评估变得复杂。我们讨论了尾部风险估计的两种主要方法：无条件分位数预测与条件分位数预测。以标普500指数为案例，我们评估了序列（极端）依赖性，执行了无条件与条件风险分析，并应用了回测检验方法。此外，本章还探讨了序列依赖性对多元尾部相依性的影响。