Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures
融合多种已实现测度的金融波动率与风险预测AI 解读 ↓
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融合多种已实现测度的金融波动率与风险预测
- 研究问题
- 面对多种各有局限的已实现波动率测度，能否通过非线性降维融合其信息，从而改善 Realised GARCH 的滚动波动率预测？
- 方法与思路
- 作者用自编码器生成综合已实现测度并纳入扩展 Realised GARCH，在四个股票市场样本上与线性降维方案比较一步预测。
- 材料陈述的贡献
- 摘要称，自编码器融合具有可行性，在所述市场及样本期的一步滚动波动率预测中优于传统线性方法，参数估计也更灵活。
- 阅读关注点
- 应核对已实现测度与日内数据处理、自编码器训练是否避免前视信息，以及滚动窗口、损失函数和比较显著性的设置。
- 阅读边界
- 这是 arXiv 预印本，摘要亦称其为学位论文，同行评审与接收未经核实；未给出数值或风险检验细节，优越性限于所述比较。
原文摘要
Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner. Our proposed model extends existing linear methods, such as Principal Component Analysis and Independent Component Analysis, to reduce the dimensionality of realised measures. The empirical evaluation, conducted across four major stock markets from January 2000 to June 2022 and including the period of COVID-19, demonstrates both the feasibility of applying an autoencoder to synthesise volatility measures and the superior effectiveness of the proposed model in one-step-ahead rolling volatility forecasting. The model exhibits enhanced flexibility in parameter estimations across each rolling window, outperforming traditional linear approaches. These findings indicate that nonlinear dimension reduction offers further adaptability and flexibility in improving the synthetic realised measure, with promising implications for future volatility forecasting applications.