|
on Network Economics |
| By: | Roncoroni, Alan; Battiston, Stefano; D'Errico, Marco; Hałaj, Grzegorz; Kok, Christoffer |
| Abstract: | We study the interplay between two channels of interconnectedness in the banking system. The first one is a direct interconnectedness, via a network of interbank loans, banks' loans to other corporate and retail clients, and securities holdings. The second channel is an indirect interconnectedness, via exposures to common asset classes. To this end, we analyze a unique supervisory data set collected by the European Central Bank that covers 26 large banks in the euro area. To assess the impact of contagion, we apply a structural valuation model NEVA (Barucca et al., 2016a), in which common shocks to banks' external assets are reflected in a consistent way in the market value of banks' mutual liabilities through the network of obligations. We identify a strongly non-linear relationship between diversification of exposures, shock size, and losses due to interbank contagion. Moreover, the most systemically important sectors tend to be the households and the financial sectors of larger countries because of their size and position in the financial network. Finally, we provide policy insights into the potential impact of more diversified versus more domestic portfolio allocation strategies on the propagation of contagion, which are relevant to the policy discussion on the European Capital Market Union. JEL Classification: C45, C63, D85, G21 |
| Keywords: | bank stress test, cross-border contagion channels, financial contagion, financial networks, financial stability, systemic risk |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20192331&r=all |
| By: | Sayantan Banerjee; Kousik Guhathakurta |
| Abstract: | A major impact of globalization has been the information flow across the financial markets rendering them vulnerable to financial contagion. Research has focused on network analysis techniques to understand the extent and nature of such information flow. It is now an established fact that a stock market crash in one country can have a serious impact on other markets across the globe. It follows that such crashes or critical regimes will affect the network dynamics of the global financial markets. In this paper, we use sequential change point detection in dynamic networks to detect changes in the network characteristics of thirteen stock markets across the globe. Our method helps us to detect changes in network behavior across all known stock market crashes during the period of study. In most of the cases, we can detect a change in the network characteristics prior to crash. Our work thus opens the possibility of using this technique to create a warning bell for critical regimes in financial markets. |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1911.05952&r=all |
| By: | Bjerre-Nielsen, Andreas |
| Abstract: | This paper investigates endogenous network formation by heterogeneous agents. The agents' types determine the value of linking and we incorporate spillovers as utility from indirect connections. We provide sufficient conditions for a class of networks with sorting to be stable for low to moderate spillovers; with only two types these networks are the unique pairwise stable ones. We also show that this sorting is suboptimal for moderate to high spillovers despite otherwise obeying the conditions for sorting in Becker 1973. This shows that in our sorted networks a tension between stability and efficiency is present. We analyze a policy tool to mitigate suboptimal sorting. |
| Date: | 2019–09–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:jn4a6&r=all |
| By: | Jie Bai (Center for International Development at Harvard University); Panle Barwick; Shengmao Cao; Shanjun Li |
| Abstract: | Are quid pro quo (technology for market access) policies effective in facilitating knowledge spillover to developing countries? We study this question in the context of the Chinese automobile industry where foreign firms are required to set up joint ventures with domestic firms in return for market access. Using a unique dataset of detailed quality measures along multiple dimensions of vehicle performance, we document empirical patterns consistent with knowledge spillovers through both ownership affiliation and geographical proximity: joint ventures and Chinese domestic firms with ownership or location linkage tend to specialize in similar quality dimensions. The identification primarily relies on within-product variation across quality dimensions and the results are robust to a variety of specifications. The pattern is not driven by endogenous joint-venture network formation, overlapping customer base, or learning by doing considerations. Leveraging additional micro datasets on part suppliers and worker flow, we document that supplier network and labor mobility are important channels in mediating knowledge spillovers. However, these channels are not tied to ownership affiliations. Finally, we calibrate a simple learning model and conduct policy counterfactuals to examine the role of quid pro quo. Our findings show that ownership affiliation facilitates learning but quality improvement is primarily driven by the other mechanisms. |
| Keywords: | Knowledge spillover |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:cid:wpfacu:368&r=all |