用于全球股票市场已实现波动率预测的图信号处理
Graph Signal Processing for Global Stock Market Realized Volatility Forecasting
AI总结：
本文提出一种集成图信号处理的已实现波动率预测框架，通过图傅里叶变换捕捉全球股市波动溢出效应，在24个全球指数数据上显著优于多种HAR基准模型。
摘要：
本文提出了一种创新的已实现波动率（RV）预测框架，通过集成图信号处理（GSP）扩展了传统的异质自回归（HAR）模型。研究首先通过分析相关的图信号能量如何追踪全球金融市场波动，评估了波动率相互关系网络的各种构建方式。随后将波动率溢出效应嵌入到所提出的框架中，该框架采用图傅里叶变换（GFT）及其逆变换，在谱域和空间域中有效捕捉全球股票市场动态。该框架不仅为建模波动率相互关系提供了全局背景，还捕捉了波动率溢出效应的非线性和方向性。使用24个全球股票市场指数的RV数据进行的实证研究，将短期、中期和长期RV预测与各种HAR型基准以及基于图神经网络的HAR模型进行了比较。所提出的模型始终优于所有对比模型，证明了将GSP集成到HAR模型中进行RV预测的有效性。
英文摘要：
This paper introduces an innovative realized volatility (RV) forecasting framework that extends the conventional Heterogeneous autoregressive (HAR) model via integrating Graph Signal Processing (GSP). The study first evaluates various constructions of volatility-interrelationship networks by analyzing how the associated graph signal energy tracks global financial market volatility. Volatility spillovers are subsequently embedded into the proposed framework, which employs the graph Fourier transform (GFT) and its inverse to effectively capture global stock market dynamics in both the spectral and spatial domains. The framework not only provides a global context for modeling the volatility interrelationships, but also captures the nonlinearity and directionality of the volatility spillover effect. The empirical study using RV data of $24$ global stock market indices compares short-, mid- and long-term RV forecasts with various HAR-type benchmarks and a graph neural network-based HAR model. The proposed model consistently outperforms all comparators, demonstrating the effectiveness of integrating GSP into the HAR model for RV forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
