|
on Network Economics |
| By: | Alessio Abeltino; Tiziano Bacaloni; Andrea Bernardini; Francesco Giancaterini; Andrea Pannone |
| Abstract: | This paper reviews the main network analysis methods used to measure structural power, which refers to the ability to shape outcomes through network position and influence, and the ability to affect others through network connections. These approaches have been applied in fields such as corporate control, global value chains, and technology supply networks. Despite significant advances, a unified framework that systematically connects these methodologies to their conceptual foundations has yet to emerge. To fill this gap, the paper introduces a taxonomy that categorizes existing methods into six families: centrality-based approaches, game-theoretic models, concentration measures, flow-based methods, optimization frameworks, and hybrid approaches that combine elements from different approaches. This classification clarifies their assumptions, analytical focus, and relative strengths, offering a coherent view of how power is structured and transmitted in complex economic and political systems. The paper concludes by outlining future research directions to refine hybrid models linking decision-making and network flows. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.10218 |
| By: | Yoon Choi |
| Abstract: | I propose an estimation algorithm for Exponential Random Graph Models (ERGM), a popular statistical network model for estimating the structural parameters of strategic network formation in economics and finance. Existing methods often produce unreliable estimates of parameters for the triangle, a key network structure that captures the tendency of two individuals with friends in common to connect. Such unreliable estimates may lead to untrustworthy policy recommendations for networks with triangles. Through a variational mean-field approach, my algorithm addresses the two well-known difficulties when estimating the ERGM, the intractability of its normalizing constant and model degeneracy. In addition, I introduce $\ell_2$ regularization that ensures a unique solution to the mean-field approximation problem under suitable conditions. I provide a non-asymptotic optimization convergence rate analysis for my proposed algorithm under mild regularity conditions. Through Monte Carlo simulations, I demonstrate that my method achieves a perfect sign recovery rate for triangle parameters for small and mid-sized networks under perturbed initialization, compared to a 50% rate for existing algorithms. I provide the sensitivity analysis of estimates of ERGM parameters to hyperparameter choices, offering practical insights for implementation. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.07176 |
| By: | Walter Farkas (University of Zurich - Department Finance; Swiss Finance Institute; ETH Zürich); Fabian Sandmeier (University of Zurich - Department of Finance; Swiss Finance Institute) |
| Abstract: | We analyze solvency and liquidity implications of Credit Default Swaps (CDS) in banking networks. We emphasize that one can neither isolate them, nor just analyze them in parallel, but needs to consider their complex interplay. By calibrating our model to the largest banks in the Euro area, we are able to run a large-scale stress test and isolate the effect of different network configurations, as well as different overall coverages of CDS, on systemic risk. An increase in CDS notional always leads to an increase in liquidity risk. The impact on solvency risk is conditional on the topology of the network. We provide a robust network configuration for which an increase in CDS notional leads to a decrease in solvency risk. |
| Keywords: | Systemic Risk, Financial Networks, Credit Default Swaps, Solvency Stress Testing |
| JEL: | C63 D85 G01 G21 G28 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp25107 |
| By: | Lorenzo Emer; Anna Gallo; Mattia Marzi; Andrea Mina; Tiziano Squartini; Andrea Vandin |
| Abstract: | Innovation emerges from complex collaboration patterns - among inventors, firms, or institutions. However, not much is known about the overall mesoscopic structure around which inventive activity self-organizes. Here, we tackle this problem by employing patent data to analyze both individual (co-inventorship) and organization (co-ownership) networks in three strategic domains (artificial intelligence, biotechnology and semiconductors). We characterize the mesoscale structure (in terms of clusters) of each domain by comparing two alternative methods: a standard baseline - modularity maximization - and one based on the minimization of the Bayesian Information Criterion, within the Stochastic Block Model and its degree-corrected variant. We find that, across sectors, inventor networks are denser and more clustered than organization ones - consistent with the presence of small recurrent teams embedded into broader institutional hierarchies - whereas organization networks have neater hierarchical role-based structures, with few bridging firms coordinating the most peripheral ones. We also find that the discovered meso-structures are connected to innovation output. In particular, Lorenz curves of forward citations show a pervasive inequality in technological influence: across sectors and methods, both inventor (especially) and organization networks consistently show high levels of concentration of citations in a few of the discovered clusters. Our results demonstrate that the baseline modularity-based method may not be capable of fully capturing the way collaborations drive the spreading of inventive impact across technological domains. This is due to the presence of local hierarchies that call for more refined tools based on Bayesian inference. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.10224 |
| By: | Martín Saldias; Sophia Mizinski |
| Abstract: | This paper explores the structure, evolution, and systemic implications of sectoral interconnectedness within the Portuguese financial system, with particular attention to the role of non-bank financial institutions (NBFIs). Using detailed quarterly whom-to-whom financial accounts from 2013 to 2024, we construct a weighted, directed, temporal multilayer network covering twelve institutional sectors and nine financial instruments. The analysis combines system-wide metrics, layer-specific topology, node-level centrality, and communitydetection methods to characterize the architecture of financial linkages over time. Results show that the Portuguese network is highly dense and remarkably stable over time, with financial intermediation dominated by a small set of core sectors, namely non-financial corporations, banks, households, government, and the rest of the world. Although NBFIs represent a relatively small share of total exposures, several subsectors—particularly insurance corporations, pension funds, and captive financial institutions—play specialized roles within specific instrument layers. Community analysis reveals three persistent modules: an external and official market-finance block, a domestic intermediation core, and a corporate equity and intra-group finance cluster. The integration of IIP and WtW data through a two-mode geography–instrument network further reveals the Rest of the World (RoW) as a structurally central funding and shock-transmission hub, organized around three coherent cross-border channels, showing that external exposures are key to the architecture of the multilayer system. Overall, the findings underscore the importance of multilayer network perspectives for macroprudential surveillance and for understanding how instrument-specific structures shape systemic risk in bank-centric financial systems. |
| JEL: | C45 F36 G01 G10 G21 G23 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202521 |
| By: | Haibo Wang; Jun Huang; Lutfu S Sua; Jaime Ortiz; Jinshyang Roan; Bahram Alidaee |
| Abstract: | The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the "too-similar-to-fail" hypothesis, arguing that risk spillovers were driven by perceived similarities in bank business models under acute interest rate pressure. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with 30-day rolling windows, a method uniquely suited for capturing the rapid network shifts inherent in a panic, we analyze daily stock returns for the four failed institutions and a systematically selected peer group of surviving banks vulnerable to the same risks from March 18, 2022, to March 15, 2023. Our results provide strong evidence for this contagion channel: total system connectedness surged dramatically during the crisis peak, and we identify SIVB, FRC, and WAL as primary net transmitters of risk while their perceived peers became significant net receivers, a key dynamic indicator of systemic vulnerability that cannot be captured by asset-by-asset analysis. We further demonstrate that these spillovers were significantly amplified by market sentiment (as measured by the VIX) and economic policy uncertainty (EPU). By providing a clear conceptual framework and robust empirical validation, our findings confirm the persistence of systemic risks within the banking network and highlight the importance of real-time monitoring in strengthening financial stability. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.01783 |
| By: | Mindy L. Mallory |
| Abstract: | We identify volatility spillovers across commodities, equities, and treasuries using a hybrid HAR-ElasticNet framework on daily realized volatility for six futures markets over 2002--2025. Our two step procedure estimates own-volatility dynamics via OLS to preserve persistence, then applies ElasticNet regularization to cross-market spillovers. The sparse network structure that emerges shows equity markets (ES, NQ) act as the primary volatility transmitters, while crude oil (CL) ends up being the largest receiver of cross-market shocks. Agricultural commodities stay isolated from the larger network. A simple univariate HAR model achieves equally performing point forecasts as our model, but our approach reveals network structure that univariate models cannot. Joint Impulse Response Functions trace how shocks propagate through the network. Our contribution is to demonstrate that hybrid estimation methods can identify meaningful spillover pathways while preserving forecast performance. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.03146 |
| By: | Masaaki Fujii (Faculty of Economics, the University of Tokyo) |
| Abstract: | Financial firms and institutional investors are routinely evaluated based on their performance relative to their peers. These relative performance concerns significantly influence risk-taking behavior and market dynamics. While the literature studying Nash equilibrium under such relative performance competitions is extensive, its effect on asset price formation remains largely unexplored. This paper investigates mean-field equilibrium price formation of a single risky stock in a discrete-time market where agents exhibit exponential utility and relative performance concerns. Unlike existing literature that typically treats asset prices as exogenous, we impose a market-clearing condition to determine the price dynamics endogenously within a relative performance equilibrium. Using a binomial tree framework, we establish the existence and uniqueness of the market-clearing mean-field equilibrium in both single- and multi-population settings. Finally, we provide illustrative numerical examples demonstrating the equilibrium price distributions and agents' optimal position sizes. |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2026cf1265 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper develops a continuous functional framework for treatment effects propagating through geographic space and economic networks. We derive a master equation from three independent economic foundations -- heterogeneous agent aggregation, market equilibrium, and cost minimization -- establishing that the framework rests on fundamental principles rather than ad hoc specifications. The framework nests conventional econometric models -- autoregressive specifications, spatial autoregressive models, and network treatment effect models -- as special cases, providing a bridge between discrete and continuous methods. A key theoretical result shows that the spatial-network interaction coefficient equals the mutual information between geographic and network coordinates, providing a parameter-free measure of channel complementarity. The Feynman-Kac representation characterizes treatment effects as accumulated policy exposure along stochastic paths representing economic linkages, connecting the continuous framework to event study methodology. The no-spillover case emerges as a testable restriction, creating a one-sided risk profile where correct inference is maintained regardless of whether spillovers exist. Monte Carlo simulations confirm that conventional estimators exhibit 25-38% bias when spillovers are present, while our estimator maintains correct inference across all configurations including the no-spillover case. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12653 |
| By: | Martin Huber; Jannis Kueck; Mara Mattes |
| Abstract: | Interference or spillover effects arise when an individual's outcome (e.g., health) is influenced not only by their own treatment (e.g., vaccination) but also by the treatment of others, creating challenges for evaluating treatment effects. Exposure mappings provide a framework to study such interference by explicitly modeling how the treatment statuses of contacts within an individual's network affect their outcome. Most existing research relies on a priori exposure mappings of limited complexity, which may fail to capture the full range of interference effects. In contrast, this study applies a graph convolutional autoencoder to learn exposure mappings in a data-driven way, which exploit dependencies and relations within a network to more accurately capture interference effects. As our main contribution, we introduce a machine learning-based test for the validity of exposure mappings and thus test the identification of the direct effect. In this testing approach, the learned exposure mapping is used as an instrument to test the validity of a simple, user-defined exposure mapping. The test leverages the fact that, if the user-defined exposure mapping is valid (so that all interference operates through it), then the learned exposure mapping is statistically independent of any individual's outcome, conditional on the user-defined exposure mapping. We assess the finite-sample performance of this proposed validity test through a simulation study. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.05728 |
| By: | Lin Chen; Yuya Sasaki |
| Abstract: | This paper studies the identification and estimation of heterogeneous effects of an endogenous treatment under interference and spillovers in a large single-network setting. We model endogenous treatment selection as an equilibrium outcome that explicitly accounts for spillovers and derive conditions guaranteeing the existence and uniqueness of this equilibrium. We then identify heterogeneous marginal exposure effects (MEEs), which may vary with both the treatment status of neighboring nodes and unobserved heterogeneity. We develop estimation strategies and establish their large-sample properties. Equipped with these tools, we analyze the heterogeneous effects of import competition on U.S. local labor markets in the presence of interference and spillovers. We find negative MEEs, consistent with the existing literature. However, these effects are amplified by spillovers in the presence of treated neighbors and among localities that tend to select into lower levels of import competition. These additional empirical findings are novel and would not be credibly obtainable without the econometric framework proposed in this paper. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.14515 |