nep-net New Economics Papers
on Network Economics
Issue of 2020‒04‒20
five papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Networking topography and default contagion in China’s financial system By Fittje, Jens; Wagner, Helmut
  2. Zero Pricing Platform Competition By Shekhar, Shiva
  3. Globalization in the Time of COVID-19 By Alessandro Sforza; Marina Steininger
  4. A new multilayer network construction via Tensor learning By Giuseppe Brandi; T. Di Matteo
  5. The illiquidity network of stocks in China's market crash By Xiaoling Tan; Jichang Zhao

  1. By: Fittje, Jens; Wagner, Helmut
    Abstract: The topography of China's financial network is unique. Is it also uniquely robust to contagion? We explore this question using network theory. We find that networks that are more concentrated are less fragile when connectivity is low. However, they remain in a robust-yet-fragile state longer than decentralized networks, when connectivity is increased. We implement Chinese characteristics into our model and simulate it numerically. The simulations show, that the large state-controlled banks act as effective stop-gaps for contagion, which makes the Chinese network relatively robust. This robustness is significantly reduced, if a significant share of the smaller banks are high-risk institutions.
    Date: 2020
  2. By: Shekhar, Shiva
    Abstract: This article studies competition between different types of ad-funded platforms attracting consumers with free services. Consumers often find advertisements a nuisance on such platforms. We study how under a competitive setting platforms balance the tension between attracting consumers and rent extraction from the advertising side. We propose a flexible yet simple model that studies competition between standard platforms and social media platforms (with same-side network effects). We find that an increase in either positive same-side network effects or an increase in consumer disutility from advertisements leads to a reduction in the number of ads on that platform. When competing platforms merge, consumer side network effects do not impact prices and the number of ads is higher. In a setting where consumers present a negative (congestion) externality on each other, competition fails to protect consumer welfare and behaves erratically. Finally, we present a few extensions and discuss some policy implications.
    Keywords: Social media platforms, platforms, two-sided markets, same side network effects, cross side network effects, advertising.
    JEL: K21 L13 L82 L86
    Date: 2020–03
  3. By: Alessandro Sforza; Marina Steininger
    Abstract: The economic effects of a pandemic crucially depend on the extend to which countries are connected in global production networks. In this paper we incorporate production barriers induced by COVID-19 shock into a Ricardian model with sectoral linkages, trade in intermediate goods and sectoral heterogeneity in production. We use our model to quantify the welfare effect of the disruption in production that started in China and then quickly spread across the world. We find that the COVID-19 shock has a considerable impact on most economies in the world, especially when a share of the labor force is quarantined. Moreover, we show that global production linkages have a clear role in magnifying the effect of the production shock. Finally, the economic effects of the COVID-19 shock are heterogeneous across sectors, regions and countries, depending on the geographic distribution of industries in each region and country and their degree of integration in the global production network.
    Keywords: COVID-19 shock, globalization, production barrier, sectoral interrelations, computational general equilibrium
    JEL: F10 F11 F14 F60
    Date: 2020
  4. By: Giuseppe Brandi; T. Di Matteo
    Abstract: Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. This methodology captures within and between connections across layers and makes use of a filtering procedure to extract relevant information and improve visualization. We show the application of this methodology to different stationary fractionally differenced financial data. We argue that our result is useful to understand the dependencies across three different aspects of financial risk, namely market risk, liquidity risk, and volatility risk. Indeed, we show how the resulting visualization is a useful tool for risk managers depicting dependency asymmetries between different risk factors and accounting for delayed cross dependencies. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified.
    Date: 2020–04
  5. By: Xiaoling Tan; Jichang Zhao
    Abstract: The stock market of China experienced an abrupt crash in 2015 and evaporated over one third of the market value. Given its associations with fear and fine-resolutions in frequency, the illiquidity of stocks may offer a promising perspective of understanding and even signaling the market crash. In this study, by connecting stocks that mutually explain illiquidity fluctuations, a illiquidity network is established to model the market. It is found that as compared to non-crash days, the market is more densely connected on crash days due to heavier but more homogeneous illiquidity dependencies that facilitate abrupt collapses. Critical socks in the illiquidity network, in particular the ones in sector of finance are targeted for inspection because of their crucial roles in taking over and passing on the losing of illiquidity. The cascading failures of stocks in market crash is profiled as disseminating from small degrees to high degrees that usually locate in the core of the illiquidity network and then back to the periphery. And by counting the days with random failures in previous five days, an early single is implemented to successfully warn more than half crash days, especially those consecutive ones at early phase. Our results would help market practitioners like regulators detect and prevent risk of crash in advance.
    Date: 2020–04

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