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on Network Economics |
| By: | Johannes Buggle; Max Deter; Martin Lange |
| Abstract: | This paper examines how network ties between local social leaders influenced the diffusion of mass protests in an autocracy. We focus on the Protestant Church and the Peaceful Revolution in East Germany. To quantify the role of leader networks in protest diffusion, we compile biographical records of over 1, 600 Protestant pastors, including their employment and education histories. Our findings reveal that network connections led to an increase in protest diffusion by up to 4.9 percentage points in a given week. Moreover, we highlight the importance of network centrality, pastors as information bridges, and the interaction with preexisting grievances and repression. |
| Keywords: | autocracy, religion, protests, networks, leaders |
| JEL: | D72 D74 N44 P16 |
| Date: | 2025–04–29 |
| URL: | https://d.repec.org/n?u=RePEc:bdp:dpaper:0064 |
| By: | Frédéric Deroïan (Aix-Marseille Univ., CNRS, AMSE, Marseille); Mohamed Belhaj (Aix-Marseille Univ., CNRS, AMSE, Marseille) |
| Abstract: | This paper introduces demotivation in the context of social comparison in networks. Social comparison is modeled as a status effect rewarding or penalizing agents according to their relative performance with respect to local peers. A demotivated agent faces both a reduced marginal return to effort and a psychological cost. In the absence of demotivation, social comparison leads to higher effort levels but reduces equilibrium welfare. Introducing demotivation leads to two main findings. First, it generates a network game of strategic substitutes. Second, despite the individual psychological costs incurred by demotivated agents, it can enhance overall welfare—by alleviating social pressure to exert effort and by generating positive externalities for peers. |
| Keywords: | Social Comparison; Demotivation; Networks; Strategic Substitutes, Equilibrium Welfare. |
| JEL: | C72 D83 D85 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2511 |
| By: | Anastasiia Antonova; Luis Huxel; Mykhailo Matvieiev; Gernot J. Muller; Gernot Müller |
| Abstract: | Imports feature at all stages of production as well as in final consumption, and this is key to how tariff shocks play out. If imposed on imports in upstream sectors, import tariffs lower domestic output in downstream sectors; if imposed downstream, they raise upstream production. The aggregate effect of tariffs can be recessionary or expansionary—depending on the strength of upstream and downstream effects. Tariffs raise inflation no matter what, but how persistently they do so also depends on the network structure. We establish these results in a New Keynesian small open-economy model with an input-output network and provide supporting evidence based on US import tariffs. Simulating the "Liberation Day" tariff package, we find it highly stagflationary. |
| Keywords: | tariffs shocks, business cycle, upstream sectors, downstream sector, input-output network, monetary policy, inflation |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11917 |
| By: | Ruixue Jing; Ryota Kobayashi; Luis Enrique Correa Rocha |
| Abstract: | The emerging cryptocurrency market presents unique challenges for investment due to its unregulated nature and inherent volatility. However, collective price movements can be explored to maximise profits with minimal risk using investment portfolios. In this paper, we develop a technical framework that utilises historical data on daily closing prices and integrates network analysis, price forecasting, and portfolio theory to identify cryptocurrencies for building profitable portfolios under uncertainty. Our method utilises the Louvain network community algorithm and consensus clustering to detect robust and temporally stable clusters of highly correlated cryptocurrencies, from which the chosen cryptocurrencies are selected. A price prediction step using the ARIMA model guarantees that the portfolio performs well for up to 14 days in the investment horizon. Empirical analysis over a 5-year period shows that despite the high volatility in the crypto market, hidden price patterns can be effectively utilised to generate consistently profitable, time-agnostic cryptocurrency portfolios. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.24831 |
| By: | Simons, J. R. |
| Abstract: | We extend the inference procedure for eigenvectors of Tyler (1981), which assumes symmetrizable matrices to generic invariant and singular subspaces of non-diagonalisable matrices to test whether {code} is an element of an invariant subspace of {code}. Our results include a Wald test for full-vector hypotheses and a t-test for coefficient-wise hypotheses. We employ perturbation expansions of invariant subspaces from Sun (1991) and singular subspaces from Liu et al. (2007). Based on the former, we extend the popular Davis-Kahan bound to estimations of its higher-order polynomials and study how the bound simplifies for eigenspaces but attains complexity for generic invariant subspaces. We apply our methods to obtain standard errors for subspace-based network statistics and degree centrality scores, when links between nodes are measured with error. We further derive convergence rates of these statistics when a network estimator has known {code}. We also derive the convergence rate for both node-wise and global clustering coefficients. Finally, we establish a formula for the density of network centrality scores based on finite-rank approximations of graphons. |
| Date: | 2025–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2530 |
| By: | Yucheng Guo; Qinxin Yan |
| Abstract: | We study particle systems interacting via hitting times on sparsely connected graphs, following the framework of Lacker, Ramanan and Wu (2023). We provide general robustness conditions that guarantee the well-posedness of physical solutions to the dynamics, and demonstrate their connections to the dynamic percolation theory. We then study the limiting behavior of the particle systems, establishing the continuous dependence of the joint law of the physical solution on the underlying graph structure with respect to local convergence and showing the convergence of the global empirical measure, which extends the general results by Lacker et al. to systems with singular interaction. The model proposed provides a general framework for analyzing systemic risks in large sparsely connected financial networks with a focus on local interactions, featuring instantaneous default cascades. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.18448 |
| By: | Junlian Gong; Jun Nagayasu |
| Abstract: | This study examines the impact of oil futures markets and production on the connectivity and speed of information transmission in a country’s spot oil market within the global network. First, we estimate the causal relationships between 12 spot oil markets using the existence and strength of transfer entropy as edges and weights to construct a series of dynamic networks for the global oil market. Second, we use a temporal network model to analyze changes in the connectivity, closeness, and betweenness of each oil market over different periods, and then compare these variations. Our findings indicate that Brent serves as the central hub of the global oil market, followed by the West Texas Intermediate and Minas markets. Moreover, the presence of oil futures markets significantly enhances the connectivity, information transmission speed, and hub role of spot markets. Oil production also positively impacts connectivity and betweenness; however, it does not have a significant relationship with the speed of information transmission. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:toh:tupdaa:71 |
| By: | Masoud Ataei |
| Abstract: | This study evaluates the scale-dependent informational efficiency of stock markets using the Financial Chaos Index, a tensor-eigenvalue-based measure of realized volatility. Incorporating Granger causality and network-theoretic analysis across a range of economic, policy, and news-based uncertainty indices, we assess whether public information is efficiently incorporated into asset price fluctuations. Based on a 34-year time period from 1990 to 2023, at the daily frequency, the semi-strong form of the Efficient Market Hypothesis is rejected at the 1\% level of significance, indicating that asset price changes respond predictably to lagged news-based uncertainty. In contrast, at the monthly frequency, such predictive structure largely vanishes, supporting informational efficiency at coarser temporal resolutions. A structural analysis of the Granger causality network reveals that fiscal and monetary policy uncertainties act as core initiators of systemic volatility, while peripheral indices, such as those related to healthcare and consumer prices, serve as latent bridges that become activated under crisis conditions. These findings underscore the role of time-scale decomposition and structural asymmetries in diagnosing market inefficiencies and mapping the propagation of macro-financial uncertainty. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.01543 |
| By: | Abeeb Olaniran (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (Corresponding author. School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study adopts a multilayer network approach to investigate the connectedness among clean, brown, and technology ETFs across four moments: returns, volatility, skewness, and kurtosis. Motivated by the non-normality of return distributions and energy transition under intensified climate risk, we demonstrate the importance of incorporating both lower- and higher-order moments to fully capture risk transmission dynamics. Within-layer, cross-layer, and total connectedness analysis reveals generally high interdependence, with notable exceptions during late 2024 (across all layers) and the 2008-2009 period (particularly for skewness and kurtosis). These episodes suggest that investor responses to extreme events differ across statistical moments, stressing the need for a multilayer framework in assessing market behaviour. While the return and volatility layers effectively capture major market shocks, skewness and kurtosis exhibit weaker spillovers, especially prior to the 2008 global financial crisis. Technology ETF plays a central role, exhibiting the highest overlap in both inflows and outflows during crisis periods, particularly between 2008 and 2014, and during COVID-19. Conversely, clean ETF shows limited vulnerability to systemic shocks, suggesting resiliency. Climate risks impact the spillovers across the within- and cross-layers. These findings are particularly relevant to investors, portfolio managers, and policymakers tasked with risk mitigation amid climate change concerns. |
| Keywords: | Clean energy, climate risk, exchange-traded funds (ETFs), spillover and multilayer network, higher-order moments, financial crises |
| JEL: | C32 G10 Q54 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202519 |
| By: | Papastaikoudis, I.; Watson, J.; Lestas, I. |
| Abstract: | We present a distributed portfolio construction framework based in network structures and combinatorial optimization. Unlike traditional centralized methods, our approach decomposes the portfolio under consideration into overlapping sub-portfolios, each reflecting a thematic strategy or mandate. This decomposition is guided by the powerset of the available set of assets, capturing all meaningful groupings and inducing a hypergraph structure where assets appear in multiple sub-portfolios. We solve the resulting distributed portfolio optimization problem using a primal-dual algorithm: primal variables represent subportfolio allocations, while dual variables emerge as shadow prices on coupling assets, offering a natural pricing interpretation and a link to microeconomic theory (general equilibrium). The framework integrates both internal portfolio signals and external models such as CAPM. We illustrate the methodology using GICS sector ETFs, constructing sub-portfolios aligned with macroeconomic themes. |
| Keywords: | Network Portfolio Theory, Mean Variance Distributed Optimization, Decentralized Pricing |
| JEL: | C61 C62 G11 G12 D85 |
| Date: | 2025–05–08 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2531 |