Fu Ouyang, Thomas T. Yang, Wenying Yao
arXiv 7 Jan 2026 · Econometrics
arXiv:2601.03598 · PDF · DOI · OpenAlex · Extracted main text
Empirical measures of financial connectedness based on Forecast Error Variance Decompositions (FEVDs) often yield dense network structures that obscure true transmission channels and complicate the identification of systemic risk. This paper proposes a novel information-criterion-based approach to uncover sparse, economically meaningful financial networks. By reformulating FEVD-based connectedness as a regression problem, we develop a model selection framework that consistently recovers the active set of spillover channels. We extend this method to generalized FEVDs to accommodate correlated shocks and introduce a data-driven procedure for tuning the penalty parameter using pseudo-out-of-sample forecast performance. Monte Carlo simulations demonstrate the approach's effectiveness with finite samples and its robustness to approximately sparse networks and heavy-tailed errors. Applications to global stock markets, S&P 500 sectoral indices, and commodity futures highlight the prevalence of sparse networks in empirical settings.
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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 | Diebold, Francis X and Yilmaz, Kamil (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 1.000 | 9 | 3 | 100% |
| 2 | Diebold, Francis X. and Yilmaz, Kamil (2009) Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets | 0.874 | 9 | 2 | 100% |
| 3 | H.Hashem Pesaran and Yongcheol Shin (1998) Generalized impulse response analysis in linear multivariate models | 0.737 | 3 | 2 | 100% |
| 4 | Ando, Tomohiro and Greenwood-Nimmo, Matthew and Shin, Yongcheol (2022) Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks | 0.644 | 2 | 2 | 100% |
| 5 | Gideon Schwarz (1978) Estimating the Dimension of a Model | 0.644 | 2 | 2 | 100% |
| 6 | Monica Billio and Mila Getmansky and Andrew W. Lo and Loriana Pelizzon (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors | 0.511 | 2 | 1 | 100% |
| 7 | Demirer, Mert and Diebold, Francis X and Liu, Laura and Yilmaz, Kamil (2018) Estimating global bank network connectedness | 0.511 | 2 | 1 | 100% |
| 8 | Helmut Lütkepohl (1990) Asymptotic Distributions of Impulse Response Functions and Forecast Error Variance Decompositions of Vector Autoregressive Models | 0.511 | 2 | 1 | 100% |
| 9 | Acemoglu, Daron and Ozdaglar, Asuman and Tahbaz-Salehi, Alireza (2015) Systemic Risk and Stability in Financial Networks | 0.405 | 1 | 1 | 100% |
| 10 | Acharya, Viral V. and Pedersen, Lasse H. and Philippon, Thomas and R… (2016) Measuring Systemic Risk | 0.405 | 1 | 1 | 100% |
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