nep-net New Economics Papers
on Network Economics
Issue of 2020‒05‒11
twelve papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. It Takes a Village: The Economics of Parenting with Neighborhood and Peer Effects By Agostinelli, Francesco; Doepke, Matthias; Sorrenti, Giuseppe; Zilibotti, Fabrizio
  2. The impact of incorrect social information on collective wisdom in human groups By Jayles, Bertrand; Escobedo, Ramon; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Théraulaz, Guy
  3. Distress propagation on production networks: Coarse-graining and modularity of linkages By Ashish Kumar; Anindya S. Chakrabarti; Anirban Chakraborti; Tushar Nandi
  4. On the excess entry theorem in the presence of network effect-sensitive consumers By Tsuyoshi Toshimitsu
  5. On the Equivalence of Neural and Production Networks By Roy Gernhardt; Bjorn Persson
  6. Hedging with Neural Networks By Johannes Ruf; Weiguan Wang
  7. Interbank risk assessment: A simulation approach By Jager, Maximilian; Siemsen, Thomas; Vilsmeier, Johannes
  8. Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages By Piero Mazzarisi; Silvia Zaoli; Carlo Campajola; Fabrizio Lillo
  9. The hyperbolic geometry of financial networks By Martin Keller-Ressel; Stephanie Nargang
  10. A perspective on correlation-based financial networks and entropy measures By Vishwas Kukreti; Hirdesh K. Pharasi; Priya Gupta; Sunil Kumar
  11. The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook By Theresa Kuchler; Dominic Russel; Johannes Stroebel
  12. Statistically validated leadlag networks and inventory prediction in the foreign exchange market By Damien Challet; Rémy Chicheportiche; Mehdi Lallouache; Serge Kassibrakis

  1. By: Agostinelli, Francesco (University of Pennsylvania); Doepke, Matthias (Northwestern University); Sorrenti, Giuseppe (University of Amsterdam); Zilibotti, Fabrizio (Yale University)
    Abstract: As children reach adolescence, peer interactions become increasingly central to their development, whereas the direct influence of parents wanes. Nevertheless, parents may continue to exert leverage by shaping their children's peer groups. We study interactions of parenting style and peer effects in a model where children's skill accumulation depends on both parental inputs and peers, and where parents can affect the peer group by restricting who their children can interact with. We estimate the model and show that it can capture empirical patterns regarding the interaction of peer characteristics, parental behavior, and skill accumulation among US high school students. We use the estimated model for policy simulations. We find that interventions (e.g., busing) that move children to a more favorable neighborhood have large effects but lose impact when they are scaled up because parents' equilibrium responses push against successful integration with the new peer group.
    Keywords: skill acquisition, peer effects, parenting, parenting style, neighborhood effects
    JEL: I24 J13 J24 R20
    Date: 2020–04
  2. By: Jayles, Bertrand; Escobedo, Ramon; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Théraulaz, Guy
    Abstract: A major problem that resulted from the massive use of social media networks is the diffusion of incorrect information. However, very few studies have investigated the impact of incorrect information on individual and collective decisions. We performed experiments in which participants had to estimate a series of quantities before and after receiving social information. Unbeknownst to them, we controlled the degree of inaccuracy of the social information through "virtual influencers", who provided some incorrect information. We find that a large proportion of individuals only partially follow the social information, thus resisting incorrect information. Moreover, we find that incorrect social information can help a group perform better when it overestimates the true value, by partly compensating a human underestimation bias. Overall, our results suggest that incorrect information does not necessarily impair the collective wisdom of groups, and can even be used to dampen the negative effects of known cognitive biases.
    Date: 2020–05
  3. By: Ashish Kumar; Anindya S. Chakrabarti; Anirban Chakraborti; Tushar Nandi
    Abstract: Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels: linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dynamical properties appear at every level, so the granularity matters. For viral epidemics, even an individual node may act as an epicenter of distress and potentially affect the entire network. Economic networks, however, are known to be immune at the micro-levels and more prone to failure in the meso/macro-levels. We propose a dynamical interaction model to characterize the mechanism of distress propagation, across different modules of a network, initiated at different epicenters. Vulnerable modules often lead to large degrees of destabilization. We demonstrate our methodology using a unique empirical data-set of input-output linkages across 0.14 million firms in one administrative state of India, a developing economy. The network has multiple hub-and-spoke structures that exhibits moderate disassortativity, which varies with the level of coarse-graining. The novelty lies in characterizing the production network at different levels of granularity or modularity, and finding `too-big-to-fail' modules supersede `too-central-to-fail' modules in distress propagation.
    Date: 2020–04
  4. By: Tsuyoshi Toshimitsu (School of Economics, Kwansei Gakuin University)
    Abstract: We reconsider the gexcess entry theorem h in the case of a network product market. Heterogeneous consumers, who are sensitive to network effects, have passive expectations and Cournot oligopolistic competition prevails in the market. We demonstrate that if the network effect elasticity of network size in the equilibrium is sufficiently large, the number of firms under free entry is socially insufficient, compared with the second-best criteria. Otherwise, the socially excessive entry arises. Furthermore, we examine the case of responsive expectations and of network effect-insensitive consumers.
    Keywords: excess entry theorem; network effect-sensitive consumers; a fulfilled expectation; Cournot oligopoly
    JEL: D42 L12 L15
    Date: 2020–04
  5. By: Roy Gernhardt; Bjorn Persson
    Abstract: This paper identifies for the first time the mathematical equivalence between economic networks of Cobb-Douglas agents and Artificial Neural Networks. It explores two implications of this equivalence under general conditions. First, a burgeoning literature has established that network propagation can transform microeconomic perturbations into large aggregate shocks. Neural network equivalence amplifies the magnitude and complexity of this phenomenon. Second, if economic agents adjust their production and utility functions in optimal response to local conditions, market pricing is a sufficient and robust channel for information feedback leading to global, macro-scale learning at the level of the economy as a whole.
    Date: 2020–05
  6. By: Johannes Ruf; Weiguan Wang
    Abstract: We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks.
    Date: 2020–04
  7. By: Jager, Maximilian; Siemsen, Thomas; Vilsmeier, Johannes
    Abstract: We introduce a novel simulation-based network approach, which provides full-edged distributions of potential interbank losses. Based on those distributions we propose measures for (i) systemic importance of single banks, (ii) vulnerability of single banks, and (iii) vulnerability of the whole sector. The framework can be used for the calibration of macro-prudential capital charges, the assessment of systemic risks in the banking sector, and for the calculation of banks' interbank loss distributions in general. Our application to German regulatory data from End-2016 shows that the German interbank network was at that time in general resilient to the default of large banks, i.e. did not exhibit substantial contagion risk. Even though up to four contagion defaults could occur due to an exogenous shock, the system-wide 99.9% VaR barely exceeds 1.5% of banks' CET 1 capital. For single institutions, however, we found indications for elevated vulnerabilities and hence the need for a close supervision.
    Keywords: Interbank contagion,credit risk,systemic risk,loss simulation
    JEL: G17 G21 G28
    Date: 2020
  8. By: Piero Mazzarisi; Silvia Zaoli; Carlo Campajola; Fabrizio Lillo
    Abstract: Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by [Hong et al., 2009]. We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.
    Date: 2020–05
  9. By: Martin Keller-Ressel; Stephanie Nargang
    Abstract: Based on data from the European banking stress tests of 2014, 2016 and the transparency exercise of 2018 we demonstrate for the first time that the latent geometry of financial networks can be well-represented by geometry of negative curvature, i.e., by hyperbolic geometry. This allows us to connect the network structure to the popularity-vs-similarity model of Papdopoulos et al., which is based on the Poincar\'e disc model of hyperbolic geometry. We show that the latent dimensions of `popularity' and `similarity' in this model are strongly associated to systemic importance and to geographic subdivisions of the banking system. In a longitudinal analysis over the time span from 2014 to 2018 we find that the systemic importance of individual banks has remained rather stable, while the peripheral community structure exhibits more (but still moderate) variability.
    Date: 2020–05
  10. By: Vishwas Kukreti; Hirdesh K. Pharasi; Priya Gupta; Sunil Kumar
    Abstract: In this brief review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. We, also, present our perspective on the use of recently developed entropy measures such as structural entropy and eigen-entropy for continuous monitoring of correlation-based networks.
    Date: 2020–04
  11. By: Theresa Kuchler; Dominic Russel; Johannes Stroebel
    Abstract: We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
    Keywords: social connectedness, COVID-19, coronavirus, communicable disease
    JEL: C60 I10
    Date: 2020
  12. By: Damien Challet (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Rémy Chicheportiche (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Mehdi Lallouache (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Serge Kassibrakis
    Abstract: We introduce a method to infer lead-lag networks of agents' actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply this method to trader-resolved data in the foreign exchange market. We show that these networks are remarkably persistent, which explains why and how order flow prediction is possible from trader-resolved data. In addition, if traders' actions depend on past prices, the evolution of the average price paid by traders may also be predictable. Using random forests, we verify that the predictability of both the sign of order flow and the direction of average transaction price is strong for retail investors at an hourly time scale, which is of great relevance to brokers and order matching engines. Finally, we argue that the existence of trader lead-lag networks explains in a self-referential way why a given trader becomes active, which is in line with the fact that most trading activity has an endogenous origin.
    Keywords: lead-lag networks,trader-resolved data,foreign exchange,prediction,inventory management
    Date: 2018–12–03

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