Improving S&P 500 Volatility Forecasting through Regime-Switching Methods
利用状态切换方法改进标普500波动率预测AI 解读 ↓
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利用状态切换方法改进标普500波动率预测
- 研究问题
- 如何利用市场状态切换和软聚类刻画结构变化，改进标普500实现波动率预测，并比较疫情前、中、后及不同递归预测期限的表现？
- 方法与思路
- 作者由五分钟收益构造日度实现波动率，比较软马尔可夫切换、分布谱聚类及 HAR 系数聚类，并以 XGBoost 预测部分状态权重。
- 材料陈述的贡献
- 摘要称基于系数的聚类算法在三个时期均优于所比较模型，并评估五日和十日递归预测，认为状态感知与软聚类有助于波动率预测。
- 阅读关注点
- 核对分段、聚类和特征工程是否只使用预测时可得信息，以及训练测试划分、各时期样本量、损失函数和模型差异的统计检验。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供预测损失数值或显著性，也未充分说明评估设计，优胜结论仅限其报告的比较。
原文摘要
Accurate prediction of financial market volatility is critical for risk management, derivatives pricing, and investment strategy. In this study, we propose a multitude of regime-switching methods to improve the prediction of S&P 500 volatility by capturing structural changes in the market across time. We use eleven years of SPX data, from May 1st, 2014 to May 27th, 2025, to compute daily realized volatility (RV) from 5-minute intraday log returns, adjusted for irregular trading days. To enhance forecast accuracy, we engineered features to capture both historical dynamics and forward-looking market sentiment across regimes. The regime-switching methods include a soft Markov switching algorithm to estimate soft-regime probabilities, a distributional spectral clustering method that uses XGBoost to assign clusters at prediction time, and a coefficient-based soft regime algorithm that extracts HAR coefficients from time segments segmented through the Mood test and clusters through Bayesian GMM for soft regime weights, using XGBoost to predict regime probabilities. Models were evaluated across three time periods--before, during, and after the COVID-19 pandemic. The coefficient-based clustering algorithm outperformed all other models, including the baseline autoregressive model, during all time periods. Additionally, each model was evaluated on its recursive forecasting performance for 5- and 10-day horizons during each time period. The findings of this study demonstrate the value of regime-aware modeling frameworks and soft clustering approaches in improving volatility forecasting, especially during periods of heightened uncertainty and structural change.