Estimation of tail risk measures in finance: Approaches to extreme value mixture modeling

2024/06/01 by Yujuan Qiu, Qiu, Yujuan
Economics, Econometrics and Finance · #FOS: Economics and business #Financial Risk and Volatility Modeling #General Economics (econ.GN) #Insurance and Financial Risk Management #Statistical Finance (q-fin.ST) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2407.05933

openalex created_date 2019/10/10 · openalex publication_date 2024/06/01 · openalex updated_date 2026/07/28

Abstract

This thesis evaluates most of the extreme mixture models and methods that have appended in the literature and implements them in the context of finance and insurance. The paper also reviews and studies extreme value theory, time series, volatility clustering, and risk measurement methods in detail. Comparing the performance of extreme mixture models and methods on different simulated distributions shows that the method based on kernel density estimation does not have an absolute superior or close to the best performance, especially for the estimation of the extreme upper or lower tail of the distribution. Preprocessing time series data using a generalized autoregressive conditional heteroskedasticity model (GARCH) and applying extreme value mixture models on extracted residuals from GARCH can improve the goodness of fit and the estimation of the tail distribution.

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