AI 中文总结
本研究开发PGA-Trans-HAR模型，结合适配预测原点的先验与空间自注意力，利用8个主要指数高频数据验证，其在跨异步市场波动率预测中精度优于基准，尤其在中长期期限增益显著。
AI 中文摘要
跨国股票市场的多元波动率预测面临一个基本信息集问题：异步交易所闭市决定了在任何预测原点下，哪些市场观测值属于信息过滤集。本研究探究正则化的、适配预测原点的跨市场信息是否能带来超出现有基准的增量精度。我们开发了PGA-Trans-HAR模型，其结合了适配预测原点的岭-VAR/GFEVD连通性先验与空间自注意力；时间不变的市场门控机制调控二者的分配，非对称注意力掩码防止闭市市场传递虚假信号，直接期限HAR基线用于锚定残差修正。利用8个主要指数2006-2022年的高频数据，我们在全交易日与共同交易日面板、5种子集集成、结构 ablation、HAC调整的Diebold-Mariano检验及模型置信集下，评估1天、5天、22天期限的直接预测。与单变量HAR相比，该框架在日度和周度期限下所有市场的均方误差（MSE）和平均绝对误差（MAE）均降低，月度期限下8个市场中有7个实现降低；在线性和深度学习基准中，它取得最低的日度平均MAE，以及周度和月度平均MSE与MAE。结构 ablation显示空间限制至关重要：学习到的市场门控在中长期期限比均匀加权提升精度，而日度预测更倾向于更强的标量收缩。适配预测原点的规范跨市场信息能产生真正的预测增益，尤其在结构溢出持续的中长期期限。
英文摘要
Forecasting global realized volatility requires a model that can learn from interconnected markets without treating zero-coded exchange closures as observed zero volatility. We develop PGA-Trans-HAR, a neuro-econometric architecture that combines a rolling ridge-VAR/GFEVD predictive-connectedness network, masked spatio-temporal attention, and a frozen HAR anchor. Missing observations used to estimate the rolling econometric prior are completed only within the trailing information set available at the forecast origin. An asymmetric source mask prevents closed markets from transmitting zero-coded closure signals. A learned, market-specific gate allocates weight between the econometric prior and data-driven spatial attention. A bounded inverse-softplus correction then refines the HAR forecast while preserving positivity. We evaluate eight international equity indices from 2006 to 2022 at 1-, 5-, and 22-union-calendar-day forecast leads, where the target is the one-day realized volatility observed at the corresponding future date. The design uses five-seed ensembles, select-and-refit estimation, structural ablations, HAC-adjusted Diebold--Mariano tests, and block-bootstrap Model Confidence Sets. PGA-Trans-HAR records the lowest cross-market average MAE at the 1-day forecast lead and the lowest average MSE and MAE at the 5- and 22-union-calendar-day forecast leads. Relative to HAR, both losses decline for all eight markets at the 1- and 5-day forecast leads and for seven markets at the 22-day forecast lead. The evidence shows that combining an origin-aligned econometric network with masked attention can improve multi-market volatility forecasts in asynchronous financial environments.
