nep-net New Economics Papers
on Network Economics
Issue of 2021‒08‒16
seven papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Dynamic Large Financial Networks via Conditional Expected Shortfalls By Giovanni Bonaccolto; Massimiliano Caporin; Bertrand Maillet
  2. The Structure of Social Relations in the Community: An Empirical Analysis for Achieving Social and Economic Inclusion By Tabuga, Aubrey D.; Cabaero, Carlos C.
  3. Estimation of subgraph densities in noisy networks By Chang, Jinyuan; Kolaczyk, Eric D.; Yao, Qiwei
  4. Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction By Chaoran Cui; Xiaojie Li; Juan Du; Chunyun Zhang; Xiushan Nie; Meng Wang; Yilong Yin
  5. Structure and oddness theorems for pairwise stable networks By Philippe Bich; Julien Fixary
  6. The Size of Micro-originated Aggregate Fluctuations: An analysis of firm-level input-output linkages in Japan By Yoshiyuki ARATA; Daisuke MIYAKAWA
  7. Identifying poverty traps based on the network structure of economic output By Vanessa Echeverri; Juan C. Duque; Daniel E. Restrepo

  1. By: Giovanni Bonaccolto (emlyon business school); Massimiliano Caporin; Bertrand Maillet
    Abstract: In this article, we first generalize the Conditional Auto-Regressive Expected Shortfall (CARES) model by introducing the loss exceedances of all (other) listed companies in the Expected Shortfall related to each firm, thus proposing the CARES-X model (where the ‘X', as usual, stands for eXtended in the case of large-dimensional problems). Second, we construct a regularized network of US financial companies by introducing the Least Absolute Shrinkage and Selection Operator in the estimation step. Third, we also propose a calibration approach for uncovering the relevant edges between the network nodes, finding that the estimated network structure dynamically evolves through different market risk regimes. We ultimately show that knowledge of the extreme risk network links provides useful information, since the intensity of these links has strong implications on portfolio risk. Indeed, it allows us to design effective risk management mitigation allocation strategies.
    Keywords: finance,Financial networks,Portfolio analysis,Systemic risk,Expectile regression
    Date: 2021–06–24
  2. By: Tabuga, Aubrey D.; Cabaero, Carlos C.
    Abstract: This paper examines the extent of social deprivation, if any, among the poor and other segments of the community. Specifically, it aims to illustrate the characteristics of social networks that poor families have through social network analysis (SNA). It inquires on the questions – How are the poor situated within the community network? Are they isolated, excluded, or integrated? To examine social inclusion or exclusion, this study uses social relations data (i.e. kinship and friendship ties) gathered in 2016 on all households residing in a rural, fishing village in the Philippines. Its primary objective is to draw insights for developing or improving efforts towards social and economic inclusion of the poor. <p> Comments to this paper are welcome within 60 days from date of posting. Email
    Keywords: Philippines, social inclusion, Social network analysis, social exclusion, inclusive development
    Date: 2020
  3. By: Chang, Jinyuan; Kolaczyk, Eric D.; Yao, Qiwei
    Abstract: While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by uncertainty quantification. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Here we study the problem of estimating the density of an arbitrary subgraph, given a noisy version of some underlying network as data. Under a simple model of network error, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. Accordingly, we develop method-of-moment estimators of network subgraph densities and error rates for the case where a minimal number of network replicates are available. These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide confidence intervals for quantifying the uncertainty in these estimates based on the asymptotic normality. To construct the confidence intervals, a new and nonstandard bootstrap method is proposed to compute asymptotic variances, which is infeasible otherwise. We illustrate the proposed methods in the context of gene coexpression networks. Supplementary materials for this article are available online.
    Keywords: bootstrap; edge density; graph; method of moments; triangles; two-stars
    JEL: C1
    Date: 2020–07–20
  4. By: Chaoran Cui; Xiaojie Li; Juan Du; Chunyun Zhang; Xiushan Nie; Meng Wang; Yilong Yin
    Abstract: Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at
    Date: 2021–07
  5. By: Philippe Bich (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne, PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Julien Fixary (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We determine the topological structure of the graph of pairwise stable weighted networks. As an application, we obtain that for large classes of polynomial payoff functions, there exists generically an odd number of pairwise stable networks. This improves the results in Bich and Morhaim ([5]) or in Herings and Zhan ([14]), and can be applied to many existing models, as for example to the public good provision model of Bramoullé and Kranton ([8]), the information transmission model of Calvó-Armengol ([9]), the two-way flow model of Bala and Goyal ([2]), or Zenou-Ballester's key-player model ([3]).
    Keywords: Weighted Networks,Pairwise Stable Networks Correspondence,Generic oddness
    Date: 2021–06
  6. By: Yoshiyuki ARATA; Daisuke MIYAKAWA
    Abstract: Recent studies (e.g., Acemoglu et al (2012)) argue that microeconomic shocks to firms propagate on input-output linkages and result in aggregate fluctuations. However, little is known about the size of micro-originated aggregate fluctuations given the empirical firm-level input-output linkages. This paper analyzes the size of micro-originated aggregate fluctuations by combining probabilistic methods and the analysis of firm-level input-output linkages in Japan. We find that due to the heterogeneity of the input-output network, microeconomic shocks account for about 30% of observed aggregate variance, consistent with the granular hypothesis. However, we find that microeconomic shocks contribute almost nothing to the tail probability of aggregate output. This is because even when the CLT does not hold, the microeconomic shocks cancel each other out "to some extent", and the resultant distribution of aggregate output is close to a Gaussian. Therefore, given the empirical input-output network in Japan, our results show that microeconomic shocks turn out to cause aggregate fluctuations of a limited size.
    Date: 2021–08
  7. By: Vanessa Echeverri; Juan C. Duque; Daniel E. Restrepo
    Abstract: In this work, we explore the relationship between monetary poverty and production combining relatedness theory, graph theory, and regression analysis. We develop two measures at product level that capture short-run and long-run patterns of poverty, respectively. We use the network of related products (or product space) and both metrics to estimate the influence of the productive structure of a country in its current and future levels of poverty. We found that poverty is highly associated with poorly connected nodes in the PS, especially products based on natural resources. We perform a series of regressions with several controls (including human capital, institutions, income, and population) to show the robustness of our measures as predictors of poverty. Finally, by means of some illustrative examples, we show how our measures distinguishes between nuanced cases of countries with similar poverty and production and identify possibilities of improving their current poverty levels.
    Date: 2021–08

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