Study on the Impact of Spectral Risk Measures on Optimizing VaR Parameter Selection

Abstract

Spectral risk measures (SRM) is an advanced financial risk measurement tool, which quantifies the possible losses of portfolio by assigning different weights to the losses of different quantiles. This paper studies how spectral risk measures affect the optimization of VaR parameters to better capture tail risk. The data used is the daily return of CSI 300 index from 2020 to 2024. The analysis process includes data processing, parameter optimization, model verification and comparative analysis. The results show that: (1) Using spectral risk measures can significantly improve the ability of VaR to capture tail risks. (2) By introducing risk aversion function and adapting to different risk preferences, spectral risk measures solves the core defect of traditional VaR in capturing tail risks.