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on Network Economics |
| By: | Ling Cheng; Qian Shao; Fengzhu Zeng; Feida Zhu |
| Abstract: | Since its advent in 2009, Bitcoin (BTC) has garnered increasing attention from both academia and industry. However, due to the massive transaction volume, no systematic study has quantitatively measured the asset decentralization degree specifically from a network perspective. In this paper, by conducting a thorough analysis of the BTC transaction network, we first address the significant gap in the availability of full-history BTC graph and network property dataset, which spans over 15 years from the genesis block (1st March, 2009) to the 845651-th block (29, May 2024). We then present the first systematic investigation to profile BTC's asset decentralization and design several decentralization degrees for quantification. Through extensive experiments, we emphasize the significant role of network properties and our network-based decentralization degree in enhancing Bitcoin analysis. Our findings demonstrate the importance of our comprehensive dataset and analysis in advancing research on Bitcoin's transaction dynamics and decentralization, providing valuable insights into the network's structure and its implications. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13603 |
| By: | Xin Zhang; Zhen Xu; Yue Liu; Mengfang Sun; Tong Zhou; Wenying Sun |
| Abstract: | In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11848 |
| By: | Shiyun Cao (Institute for Economic and Social Research, Jinan University); Jennifer T. Lai (School of Finance, Guangdong University of Foreign Studies); Paul D. McNelis (Boston College) |
| Abstract: | This paper assesses the network connectedness of risks in China’s stock market, focusing on how shocks in the real estate sector impact financial institutions. We analyze the effect of financial instability in real estate firms on the stability of the broader financial system. To measure the transmission of these risks, we use two key methods: generalized forecast error variance decomposition and the ∆CoVaR approach.Our findings reveal that banks often serve as net receivers of risk, while non-bank financial institutions amplify the transmission of real estate-related risks. This highlights the critical role of non-banks in propagating risk throughout the financial system and underscores the importance of robust systemic risk monitoring across financial networks. |
| Keywords: | financial contagion, China, real estate, regulation |
| JEL: | G21 G22 G23 G28 |
| Date: | 2024–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1083 |
| By: | Patrick Gruning (Latvijas Banka); Zeynep Kantur (Baskent University) |
| Abstract: | This paper introduces financial intermediaries, who engage in lending to firms for investments and buying public bonds issued by the government, and unconventional monetary policy in the form of quantitative easing or tightening into a rich New- Keynesian multi-sector E-DSGE model with production and investment networks. Due to the strong input-output linkages between sectors, almost all policies are found to be not effective in facilitating a green transition. The policies considered are sector-specific bank regulation policies, unconventional monetary policies, various carbon tax revenue recycling schemes, public green capital investment, and sector- specific investment tax/subsidy policies. Only if carbon tax revenues are used to build public green capital, thereby boosting productivity of the green sectors, the trade-off between achieving positive economic growth and reducing carbon emissions is fully resolved. |
| Keywords: | Production network, Investment network, Climate change, Financial intermediation, Financial stability, Stranded assets, Monetary policy |
| JEL: | E22 E32 E52 G21 L14 Q50 |
| Date: | 2024–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:ltv:wpaper:202406 |
| By: | Giovanni Carnazza; Paolo Liberati; Agnese Sacchi |
| Abstract: | In recent times, many countries have continued to deal with political instability due to difficulties in improving democratic practices and limiting episodes of violence and terrorism. Using a sample of 27 European Union (EU) countries observed yearly during the period 1999-2021, we empirically analyze how the domestic political instability of a given country can be affected by the degree of trade diversification adjusted for the political instability of the nonEU countries it trades with. We adopt a network-based approach and build a novel geopolitical dependency index. We find there is a risk of importing political instability along with international trade by increasing trade concentration or the import share from more politically unstable non-EU countries. Given the relevance of the United States and China for European economic activity, we also test our main hypothesis by adjusting the geopolitical dependency index. We see China’s prominent role in trade and political tension in EU countries compared to the US. |
| Keywords: | political instability, trade diversification, network analysis, geopolitical dependency, EU countries |
| JEL: | D74 D85 F10 F50 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:pie:dsedps:2024/319 |