网络实现GARCH--Itô模型：高频识别下的波动率溢出
Network Realized GARCH--Itô Models: Volatility Spillovers with High-Frequency Identification
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中文总结 AI 辅助
提出网络实现GARCH-Itô模型，利用高频日内收益识别多资产波动率传导网络，正则化选择结构，实证发现能源为净发送者且总体连通性稳定。
中文摘要 AI 辅助
我们引入了一种网络实现GARCH-Itô模型，其中波动率传导是多个资产潜在日度已实现波动率之间的动态关系。一个未知的有向且带符号的网络被嵌入到连续时间方差过程中，并出现在由此产生的指数日度递推式中。日内收益识别出已实现波动率，从而识别出控制其传播的网络。对于固定的网络模板，基于已实现波动率的可行估计与基于潜在已实现波动率的估计在一阶意义上等价。对于未知网络，正则化方法选择相关结构，并且在预言机条件下，固定秩的局部重拟合为基于模型隐含响应的连通性提供条件推断。在对2007年至2025年间九只美国行业ETF的应用中，该模型在报告的递归预测中取得了最低的平均样本外QLIKE，尽管其相对于HAR的优势在统计上并不显著。在全样本中，加权LASSO选择了一个空的稀疏支持集，因此推断关注于秩一投影因子成分；这一条件分析将能源板块识别为净波动率发送者。总体连通性在不同波动率度量下比单个稀疏渠道更为稳定。
英文摘要
We introduce a network realized GARCH-Itô model in which volatility transmission is a dynamic relation among the latent daily integrated volatilities of multiple assets. An unknown directed and signed network is embedded in a continuous-time variance process and appears in the resulting exponential daily recursion. Intraday returns identify integrated volatility and hence the network that governs its propagation. For a fixed network template, feasible estimation based on realized volatility is first-order equivalent to estimation based on latent integrated volatility. For an unknown network, regularization selects the relevant structure, and, under oracle conditions, a fixed-rank local refit provides conditional inference for model-implied response-based connectedness. In an application to nine U.S. sector ETFs from 2007 to 2025, the model attains the lowest average out-of-sample QLIKE among the reported recursive forecasts, although its advantage over HAR is not statistically significant. In the full sample the weighted LASSO selects an empty sparse support, so inference concerns the rank-one projected factor component; this conditional analysis identifies Energy as a net volatility transmitter. Aggregate connectedness is more stable across volatility measures than individual sparse channels.
发表机构
- Shanghai University of Finance and Economics（上海财经大学）
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