We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks.

翻译：我们提出了一种非参数时变有向信息图（TV-DIG）框架，用于估计时间序列网络中不断演化的因果结构，从而克服传统计量模型在高维、非线性及动态互联关系捕捉方面的局限性。该框架采用基于广义格兰杰因果检验的信息论测度，可同时应用于线性和非线性动态系统。本框架在系统性风险度量方面取得进展，并与向量自回归和状态转换模型等经典计量模型建立了有意义的联系。通过仿真实验和实证分析验证模型有效性，我们在恢复具有非线性多变量结构的时变网络方面取得了令人满意的结果。我们将该框架应用于识别和监测金融网络中主要资产与行业板块之间的关联演化及系统性风险，重点研究加密货币对金融稳定性的潜在系统性威胁，包括新冠疫情期间及美联储2020年紧急应对措施中向其他部门的风险溢出效应。研究发现：加密货币在2020年前对特定金融部门存在此前未被充分认识的显著影响，揭示了其潜在系统性风险，并为追踪金融网络中不断演变的跨部门交互作用提供了系统性方法。