AI 中文总结
本文提出LASSO惩罚的准极大似然估计方法，联合学习英国股市的波动率依赖网络，提升了样本外波动率预测精度，揭示了替代结构未捕捉的方向依赖关系。
AI 中文摘要
时空ARCH模型可捕捉波动率的时间持续性与截面依赖关系，但通常需要预先定义空间权重矩阵，这在金融市场中存在局限——其依赖网络往往未知。本文提出一种LASSO惩罚的准极大似然估计方法，可联合学习稀疏权重矩阵并估计时间依赖关系与协变量效应。蒙特卡洛实验表明，该方法能恢复模型参数与潜在网络，且随时间样本量增大精度提升。将该方法应用于20家英国上市公司的日收益率数据，对比学习所得网络与欧氏距离、相关性、自回归相似度及行业结构，发现其在样本外交动率预测中表现更优，且揭示了预先定义的替代结构未捕捉到的企业层面及跨行业方向依赖关系，为学习可解释的条件波动率网络提供了数据驱动框架。
英文摘要
Spatiotemporal ARCH models capture temporal volatility persistence and cross-sectional dependence but typically require a predefined spatial weight matrix. This is restrictive in financial markets, where the dependence network is rarely known. We develop a LASSO-penalised quasi-maximum likelihood estimator that jointly learns a sparse weight matrix and estimates temporal dependence and covariate effects. Monte Carlo experiments show that the method recovers the model parameters and underlying network, with accuracy improving as the temporal sample size increases. We apply the method to daily returns from twenty UK-listed firms and compare the learned network with Euclidean-distance, correlation, autoregressive-similarity and sector-based structures. The learned network improves out-of-sample volatility prediction and reveals directional firm-level and cross-sector dependence not captured by the predefined alternatives. The method provides a data-driven framework for learning interpretable conditional-volatility networks.
Comments25 pages, 7 Figures
