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A Network Analysis of the Sectoral From-Whom-To-Whom Financial Stock Matrix

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Global Flow of Funds Analysis

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Abstract

This study enhances global flow of funds (GFF) statistics for assessing global financial stability at the national and cross-border sectoral levels. The investigation involves scrutinizing data sources and reconstructing the statistical framework to establish the sectoral from-whom-to-whom financial stock matrix (SFSM). The SFSM is constructed using sectoral account data, complemented by international statistics from the Coordinated Portfolio Investment Survey, International Investment Position, and Bank for International Settlements. The SFSM specifically focuses on counterparty national and cross-border exposures of sectors in China, Japan, the United Kingdom, and the United States. It is designed to create country-specific financial networks, interconnecting each country-level network based on cross-border exposures. Analytical results systematically reveal bilateral exposures among the four countries in the GFF, identifying sectoral interlinkages, characteristics of overseas investment, external shocks, and internal influences. Furthermore, this study introduces an eigenvector decomposition to analyze the effects and provided an analytical description of the shock dynamics and propagation process.

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Notes

  1. 1.

    They are (i) build-up of risk in the financial sector, (ii) cross-border financial linkages, (ii) vulnerability of domestic economies to shocks, and (iv) improvement in communication of official statistics.

  2. 2.

    OECD. Stat (2021) Dataset: 720. Financial balance sheets, non-consolidated, SNA 2008, https://stats.oecd.org/.

  3. 3.

    These include currency, deposits, loans, undiscounted bankers’ acceptance bills, technical insurance reserves, interfinancial institution accounts, required and excess reserves, bonds, equity and share, security investment fund, central bank loans, foreign direct investment (FDI), changes in reserve assets, and miscellaneous (net). See The People’s Bank of China (2022) Financial Assets and Liabilities Statement (Financial Accounts), http://www.pbc.gov.cn/en/3688247/3688975/4280784/index.html.

  4. 4.

    See Table A6.2, https://stats.bis.org/statx/toc/LBS.html.

  5. 5.

    Table 6: Reported Portfolio Investment Assets by Sector of Holder, and Sector and Economy of Nonresident Issuer for Specified Economies.

  6. 6.

    For the calculation method of Table Y refer to Zhang (2020), 108–110.

  7. 7.

    To avoid double counting, the claims, that is, loans and deposits of CN to the US in Table A6.2-S banks’’ cross-border positions on residents of CN in the LBS account, are subtracted from the claims of FC by ROW in SFSM (see Table 7).

  8. 8.

    CPIS: Table 6, Reported Portfolio Investment Assets by Sector of Holder, and Sector and Economy of Nonresident Issuer for Specified Economies, December 2018.

  9. 9.

    This is the arrangement of rows and columns which designed by Stone’s formula (see Chap. 3).

  10. 10.

    The ROW sector in the FA is designed from a foreign standpoint, so its assets and liabilities show an opposite relationship when observed from the domestic standpoint, and its assets are the liabilities of the domestic.

  11. 11.

    See Table 7–9 of Zhang (2022).

  12. 12.

    See Table 7–9 of Zhang (2022).

  13. 13.

    As of the end of November 2023, China has not released the FBS data for 2022, so the time series of FBS data for various sectors in China can only be from 2017 to 2021.

  14. 14.

    It was Rasmussen (1956) who invented the dispersion indices for the input–output analysis. While the PDI is the mean-normalized column sum, the SDI is the mean-normalized row sum of the Leontief inverse.

  15. 15.

    Leontief (1953, 1963).

  16. 16.

    Leontief (1941).

  17. 17.

    The data for the ROW sector in Table 5.9 refer to the amount of financial assets or liabilities held with other economies (excluding the G4), so the ROW sectors in the G-4 network have a lower EC value.

  18. 18.

    The ROW sector here means external investment and financing in addition to G-4.

  19. 19.

    Nan Zhang (2022).

  20. 20.

    Zhang (2022).

  21. 21.

    Eigenvalues, eigenvectors, and shocks in the base of eigenvectors are in general complex numbers if we allow for diffusion matrices that are diagonalizable in the complex plane. This case pertains to economic analysis. When the eigenvalue or eigenvector exhibits a very small imaginary component, considering only the real part of the complex number does not significantly impact the accuracy of prediction. Therefore, this paper exclusively focuses on the real component of the complex number.

  22. 22.

    IMF (2016); Carlos Sanchez-Munoz, Artak Harutyunyan, Padma S Hurree Gobin (2022).

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Author information

Authors and Affiliations

  1. Hiroshima Shudo University, Hiroshima, Japan

    Nan Zhang

  2. Cornell University, New York, NY, USA

    Yiye Zhang

Authors
  1. Nan Zhang
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  2. Yiye Zhang
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Correspondence to Nan Zhang .

Appendix A

Appendix A

See Tables A.1, A.2, A.3 and A.4.

Table A.1 International SFSM with sectoral data (at the end of 2018, USD bn.)
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Table A.2 International SFSM with sectoral data (at the end of 2019, USD bn.)
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Table A.3 International SFSM with sectoral data (at the end of 2020, USD bn.)
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Table A.4 International SFSM with sectoral data (at the end of 2021, USD bn.)
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Zhang, N., Zhang, Y. (2024). A Network Analysis of the Sectoral From-Whom-To-Whom Financial Stock Matrix. In: Global Flow of Funds Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-97-1029-4_5

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