论文标题
通过SV-Garch-EVT模型进行数据驱动的风险测量
Data-Driven Risk Measurement by SV-GARCH-EVT Model
论文作者
论文摘要
本文旨在通过准确表征金融市场回报和波动性来更有效地管理和减轻股票市场风险。我们通过结合脂肪尾分布并利用效果来增强随机波动率（SV）模型，并使用马尔可夫链蒙特卡洛（MCMC）方法估算模型参数。通过整合极值理论（EVT）以适合标准残差的尾部分布，我们开发了基于SV-EVT-VAR的动态模型。我们使用每日S \＆P 500索引数据和模拟回报的经验分析表明，基于SV-EVT的模型的表现优于其他其他模型。这些模型有效地捕获了财务收益的脂肪尾部特性和杠杆作用，证明了样本外数据分析的优越性。
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.
