Jalal Etesami, Ali Habibnia, Negar Kiyavash
arXiv 27 Dec 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2312.16707 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 83% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | D. Bianchi, M. Billio, R. Casarin, and M. Guidolin (2019) Modeling systemic risk with markov switching graphical sur models | 1.000 | 7 | 3 | 100% |
| 2 | M. Billio, M. Getmansky, A. W. Lo, and L. Pelizzon (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors | 1.000 | 5 | 4 | 100% |
| 3 | F. X. Diebold and K. Ylmaz (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 0.928 | 4 | 3 | 100% |
| 4 | F.-b. Lu, Y.-m. Hong, S.-y. Wang, K.-k. Lai, and J. Liu (2014) Time-varying granger causality tests for applications in global crude oil markets | 0.811 | 4 | 2 | 100% |
| 5 | J. Etesami and N. Kiyavash (2014) Directed information graphs: A generalization of linear dynamical graphs | 0.737 | 4 | 2 | 75% |
| 6 | Y. Hong (2001) A test for volatility spillover with application to exchange rates | 0.737 | 3 | 2 | 100% |
| 7 | M. Billio and S. Di Sanzo (2015) Granger-causality in markov switching models | 0.644 | 4 | 1 | 100% |
| 8 | U. Kruger, J. Zhang, and L. Xie (2008) Developments and applications of nonlinear principal component analysis-a review | 0.644 | 3 | 2 | 67% |
| 9 | G. Bonaccolto, M. Caporin, and R. Panzica (2019) Estimation and model-based combination of causality networks among large us banks and insurance companies | 0.644 | 2 | 2 | 100% |
| 10 | J. Etesami, A. Habibnia, and N. Kiyavash (2017) Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 93 scored citations.