Modeling Systemic Risk: A Time-Varying Nonparametric Causal Inference Framework
This work addresses systemic risk modeling for financial stability, offering a systematic approach to track evolving cross-sector interactions, though it is incremental in improving existing econometric models.
The authors tackled the problem of estimating evolving causal structures in time series networks to capture high-dimensional, nonlinear, and time-varying interconnections, proposing a TV-DIG framework that showed promising results in recovering simulated networks and identified significant pre-2020 influences of cryptocurrencies on financial sectors.
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.