Tail Risk Analysis for Financial Time Series

2024/09/27 by Anna Kiriliouk, Zhou Chen, Kiriliouk, Anna +1
Decision Sciences · Economics, Econometrics and Finance · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Insurance and Financial Risk Management #Methodology (stat.ME) #Risk Management (q-fin.RM) #Risk and Portfolio Optimization

paper · pdf · doi:10.48550/arxiv.2409.18643

openalex publication_date 2024/09/27 · openalex created_date 2024/10/27 · openalex updated_date 2026/07/28

Abstract

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.

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