Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
系统性风险建模：时变非参数因果推断框架AI 解读 ↓
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系统性风险建模：时变非参数因果推断框架
- 研究问题
- 如何刻画金融时间序列间高维、非线性且随时间变化的联系，并据此追踪加密货币与其他金融部门的系统性风险？
- 方法与思路
- 作者提出时变有向信息图，以广义 Granger 因果的信息论度量估计序列网络，并通过模拟和金融资产数据分析评估。
- 材料陈述的贡献
- 摘要称方法能够恢复模拟中的时变非线性网络，并报告加密货币在二〇二〇年前对部分金融部门存在此前低估的影响。
- 阅读关注点
- 应核对信息度量的估计和时变处理、网络边的统计检验，以及预测性联系是否足以支持所使用的因果和风险解释。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明结构因果识别条件、风险度量细节或结果数值。
原文摘要
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.