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Data-Driven Risk Measurement by SV-GARCH-EVT Model

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  • Minheng Xiao

Abstract

This paper aims to more effectively manage and mitigate stock market risks by accurately characterizing financial market returns and volatility. We enhance the Stochastic Volatility (SV) model by incorporating fat-tailed distributions and leverage effects, estimating model parameters using Markov Chain Monte Carlo (MCMC) methods. By integrating extreme value theory (EVT) to fit the tail distribution of standard residuals, we develop the SV-EVT-VaR-based dynamic model. Our empirical analysis, using daily S\&P 500 index data and simulated returns, shows that SV-EVT-based models outperform others in backtesting. These models effectively capture the fat-tailed properties of financial returns and the leverage effect, proving superior for out-of-sample data analysis.

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  • Minheng Xiao, 2022. "Data-Driven Risk Measurement by SV-GARCH-EVT Model," Papers 2201.09434, arXiv.org, revised Dec 2024.
    • Handle: RePEc:arx:papers:2201.09434
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