Systemic risk and macro-financial interconnectedness using an FSAM framework

References

• Achjar, N., Sonis, M., & Hewings, G. J. D. (2006). Structural changes in the Indonesian economy: A network complication approach. Journal of Applied Input–Output Analysis, 11, 91–119.
   Google Scholar
• Aldasoro, I., & Angeloni, I. (2015). Input–output-based measures of systemic importance. Quantitative Finance, 15(4), 589–606. doi:10.1080/14697688.2014.968194
   Web of Science ®Google Scholar
• Amiti, M., & Weinstein, D. E. (2018). How much do idiosyncratic bank shocks affect investment? Evidence from matched bank-firm loan data. Journal of Political Economy, 126(2), 525–587. doi:10.1086/696272
   Web of Science ®Google Scholar
• Aray, H., Pedauga, L., & Velázquez, A. (2017). Financial Social Accounting Matrix: a useful tool for understanding the macro-financial linkages of an economy. Economic Systems Research, 29(4), 486–508. doi:10.1080/09535314.2017.1365049
   Web of Science ®Google Scholar
• Banco de España. (2019). Cuentas Financieras de la Economía Española. www.bde.es (observed 5 May 2020).
   Google Scholar
• Basel Committee on Banking Supervision. (2011). Global systemically important banks: Assessment methodology and the additional loss absorbency requirement, Bank for International Settlements, Basel, November.
   Google Scholar
• Basel Committee on Banking Supervision. (2017a). Standards: Pillar 3 disclosure: requirements – consolidated and enhanced framework. Bank for International Settlements, Basel, March.
   Google Scholar
• Basel Committee on Banking Supervision. (2017b). Global systemically important banks - revised assessment framework, Consultative document, Bank for International Settlements, Basel, March.
   Google Scholar
• Basel Committee on Banking Supervision. (2018). Global systemically important banks: revised assessment methodology and the higher loss absorbency requirement, Bank of International Settlements, Basel, July.
   Google Scholar
• Beck, T. (2020). Finance in times of COVID-19: What next. Mitigating the Covid Economic Crisis: Act Fast and Do Whatever It Takes, edited by R. Baldwin and BW di Mauro, VOXEu. org Book.
   Google Scholar
• Bezemer, D. J. (2010). Understanding financial crisis through accounting models. Accounting, Organizations and Society, 35(7), 676–668. doi:10.1016/j.aos.2010.07.002
   Web of Science ®Google Scholar
• Blank, S., Buch, C. M., & Neugebauer, K. (2009). Shocks at large banks and banking sector distress: The Banking granular residual. Journal of Financial Stability, 5(4), 353–373. doi:10.1016/j.jfs.2008.12.002
   Web of Science ®Google Scholar
• Borio, C. (2013). The great financial crisis: Setting priorities for new statistics. Journal of Banking Regulation, 14(3–4), 306–317. doi:10.1057/jbr.2013.9
   Google Scholar
• Bremus, F., & Buch, C. M. (2017). Granularity in banking and growth: Does financial openness matter? Journal of Banking & Finance, 77, 300–316. doi:10.1016/j.jbankfin.2016.04.023
   Web of Science ®Google Scholar
• Bremus, F., Buch, C. M., Russ, K. N., & Schnitzer, M. (2018). Big banks and macroeconomic outcomes: Theory and cross-country evidence of granularity. Journal of Money, Credit and Banking, 50(8), 1785–1825. doi:10.1111/jmcb.12545
   Web of Science ®Google Scholar
• Bucci, A., La Torre, D., Liuzzi, D., & Marsiglio, S. (2019). Financial contagion and economic development: An epidemiological approach. Journal of Economic Behavior & Organization, 162, 211–228. doi:10.1016/j.jebo.2018.12.018
   Web of Science ®Google Scholar
• Buch, C. M., & Neugebauer, K. (2011). Bank-specific shocks and the real economy. Journal of Banking & Finance, 35(8), 2179–2187. doi:10.1016/j.jbankfin.2011.01.023
   Web of Science ®Google Scholar
• Burgess, S. (2011). Measuring financial sector output and its contribution to UK GDP. Bank of England Quarterly Bulletin, 51(3), 234–246.
   Google Scholar
• Cai, J., & Leung, P. (2004). Linkage measures: A revisit and a suggested alternative. Technology Analysis & Strategic Management, 16(1), 63–83. doi:10.1080/0953531032000164800a
   Google Scholar
• Carbó-Valverde, S., Humphrey, D., & Rodríguez, F. (2003). Deregulation, bank competition and regional growth. Regional Studies, 37(3), 227–237. doi:10.1080/0034340032000065398
   Web of Science ®Google Scholar
• Carbó-Valverde, S., & Rodríguez, F. (2004). The finance-growth nexus: A regional perspective. European Urban and Regional Studies, 11(4), 339–354. doi:10.1177/0969776404046265
   Web of Science ®Google Scholar
• Cardenete, M., Lima, M., & Sancho, F. (2013). Are there key sectors? An appraisal using applied general equilibrium. Review of Regional Studies, 43(2, 3), 111–129. https:/doi.org/10.52324/001c.8087
   Google Scholar
• Cardenete, M., & Sancho, F. (2006). Missing links in key sector analysis. Economic Systems Research, 18(3), 319–325. doi:10.1080/09535310600844409
   Google Scholar
• Clements, B. J. (1990). On the decomposition and normalization of interindustry linkages. Economics Letters, 33(4), 337–340. doi:10.1016/0165-1765(90)90084-E
   Web of Science ®Google Scholar
• Defourny, J., & Thorbecke, E. (1984). Structural path analysis and multiplier decomposition within a social accounting matrix framework. The Economic Journal, 94(373), 111–136. doi:10.2307/2232220
   Web of Science ®Google Scholar
• Demirgüç-Kunt, A., & Levine, R. (2018). Finance and growth. Edward Elgar Publishing Limited.
   Google Scholar
• Demirgüç-Kunt, A., Pedraza, A., & Ruiz Ortega, C. (2020). Banking sector performance during the covid-19 crisis. World Bank Policy Research Working Paper, 9363.
   Google Scholar
• Didier, T., Huneeus, F., Larrain, M., & Schmukler, S. L. (2021). Financing firms in hibernation during the COVID-19 pandemic. Journal of Financial Stability, 53, 100837. doi:10.1016/j.jfs.2020.100837
   Web of Science ®Google Scholar
• Dietzenbacher, E. (2006). Multiplier estimates: To bias or not to bias? Journal of Regional Science, 46(4), 773–786. doi:10.1111/j.1467-9787.2006.00477.x
   Web of Science ®Google Scholar
• Dietzenbacher, E., & Lahr, M. L. (2013). Expanding extractions. Economic Systems Research, 25(3), 341–360. doi:10.1080/09535314.2013.774266
   Web of Science ®Google Scholar
• Dietzenbacher, E., van Burken, B., & Kondo, Y. (2019). Hypothetical extractions from a global perspective. Economic Systems Research, 31(4), 505–519. https://doi.org/10.1080/09535314.2018.1564135
   Web of Science ®Google Scholar
• Dietzenbacher, E., & Van der Linden, J. A. (1997). Sectoral and spatial linkages in the EC production structure. Journal of Regional Science, 37(2), 235–257. doi:10.1111/0022-4146.00053
   Web of Science ®Google Scholar
• Duffy, J., Karadimitropoulou, A., & Parravano, M. (2019). Financial contagion in the laboratory: Does network structure matter? Journal of Money, Credit and Banking, 51(5), 1097–1136. doi:10.1111/jmcb.12563
   Web of Science ®Google Scholar
• Emini, C. A., & Fofack, H. (2004). A financial social accounting matrix for the integrated macroeconomic model for poverty analysis: Application to Cameroon with a fixed-price multiplier analysis. Vol. 3219. World Bank Publications.
   Google Scholar
• Evans, O., Leone, A. M., Gill, M., & Hilbers, P. (2000). Macroprudential Indicators of Financial System Soundness, IMF Occasional Paper, 192.
   Google Scholar
• Fernandez de Guevara, J., & Maudos, J. (2007). Regional financial development and bank competition: effects on economic growth, MPRA Paper 15255, University Library of Munich, Germany.
   Google Scholar
• Financial Stability Board. (2018). Global Shadow Banking Monitoring Report 2017. Financial Stability Board: Basel.
   Google Scholar
• Freytag, A., & Fricke, S. (2017). Sectoral linkages of financial services as channels of economic development – An input–output analysis of the Nigerian and Kenyan economies. Review of Development Finance, 7(1), 36–44. doi:10.1016/j.rdf.2017.01.004
   Web of Science ®Google Scholar
• Gabaix, X. (2011). The granular origins of aggregate fluctuations. Econometrica, 79(3), 733–772. doi:10.3982/ECTA8769
   Web of Science ®Google Scholar
• Guerra, A. I., & Sancho, F. (2010). Measuring energy linkages with the hypothetical extraction method: An application to Spain. Energy Economics, 32(4), 831–837. doi:10.1016/j.eneco.2009.10.017
   Web of Science ®Google Scholar
• Hanson, K. A., & Robinson, S. (1991). Data, linkages and models: US national income and product accounts in the framework of a social accounting matrix. Economic Systems Research, 3(3), 215–232. doi:10.1080/09535319100000019
   Google Scholar
• Harutyunyan, A., & Sánchez, C. (2019). The IMF balance sheet approach: Towards from-whom-to-whom information on cross-border portfolio securities. IFC Bulletins Chapters, 49, 300–317.
   Google Scholar
• Hazari, B. R. (1970). Empirical identification of key sectors in the Indian economy. The Review of Economics and Statistics, 52(3), 301–305. doi:10.2307/1926298
   Web of Science ®Google Scholar
• Heath, R., & Goksu, E. (2016). G-20 Data gaps initiative II: Meeting the policy challenge, IMF Working Paper, WP/16/43.
   Google Scholar
• Heath, R., & Goksu, E. (2017). Financial stability analysis: What are the data needs? IMF Working Paper 17/153, International Monetary Fund, Washington, DC.
   Google Scholar
• Hirschman, Albert (1958). The strategy of economic development. New Haven, Conn.: Yale Univ. Press.
   Google Scholar
• Hubic, A. (2012). A Financial Social Accounting Matrix (SAM) for Luxembourg, Working Paper, 2008–72, Central Bank of Luxembourg.
   Google Scholar
• International Monetary Fund & Financial Stability Board. (2009). The financial crisis and information gaps, Report to the G20 Finance Ministers and Central Bank Governors, October.
   Google Scholar
• International Monetary Fund & Financial Stability Board. (2015). The Financial Crisis and Information Gaps: Sixth Progress Report on the Implementation of the G-20 Data Gaps Initiative, technical report, September, http://www.fsb.org/wp-content/uploads/The-Financial-Crisis-and-Information-Gaps.pdf.
   Google Scholar
• International Monetary Fund & Financial Stability Board. (2018). Implementation and Effects of the G20 Financial Regulatory Reforms: 5th Annual Report. Basel.
   Google Scholar
• International Monetary Fund & Financial Stability Board. (2019). G20 Data Gaps Initiative (DGI-2): The Fourth Progress Report - Countdown to 2021. Basel.
   Google Scholar
• Jamshidian, M., & Mata, M. (2007). Advances in analysis of mean and covariance structure when data are incomplete. In Handbook of latent variable and related models (pp. 21–44). North-Holland.
   Google Scholar
• Jansen, P. (1994). Analysis of multipliers in stochastic input-output models. Regional Science and Urban Economics, 24(1), 55–74. doi:10.1016/0166-0462(94)90019-1
   Web of Science ®Google Scholar
• Kasinger, J., & Pelizzon, L. (2018). Financial stability in the EU: A case for micro data transparency. SAFE Policy Letter, 67, 1–9.
   Google Scholar
• Lenzen, M. (2003). Environmentally important paths, linkages and key sectors in the Australian economy. Structural Change and Economic Dynamics, 14(1), 1–34. doi:10.1016/S0954-349X(02)00025-5
   Google Scholar
• Lenzen, M. (2007). Structural path analysis of ecosystem networks. Ecological Modelling, 200(3–4), 334–342. doi:10.1016/j.ecolmodel.2006.07.041
   Web of Science ®Google Scholar
• Lima, F., & Drumond, I. (2016). How to keep statistics’ customers happy? Use micro-databases!. IFC Bulletin No 41. Bank of International Settlements.
   Google Scholar
• Luo, J. (2013). Which industries to bail out first in economic recession? Ranking US industrial sectors by the power-of-pull. Economic Systems Research, 25(2), 157–169. doi:10.1080/09535314.2013.775111
   Web of Science ®Google Scholar
• Lütkepohl, H. (2008). Impulse response function. In P. Macmillan (Ed.), The New Palgrave dictionary of economics. Palgrave Macmillan. https://doi.org/10.1057/978-1-349-95121-5_2410-1
   Google Scholar
• Miller, R. E. (1966). Interregional feedback effects in input-output models: Some preliminary results. Papers of the Regional Science Association, 17(1), 105–125. doi:10.1007/BF01982512
   Google Scholar
• Miller, R. E., & Blair, P. D. (2009). Input-output analysis: Foundations and extensions. Cambridge University Press.
   Google Scholar
• Miller, R. E., & Lahr, M. L. (2001). A taxonomy of extractions. In M. L. Lahr & R. E. Miller (Eds.), Regional science perspectives in economic analysis – a festschrift in memory of Benjamin H. Stevens (pp. 407–441). North-Holland.
   Google Scholar
• Paelinck, J., De Caevel, J., & Degueldre, J. (1965). Analyse quantitative de certaines phénomenes du développment régional polarisé: Essai de simulation statique d’itérarires de propogation. Bibliothèque de L’Institut de Science économique, 7, 341–387.
   Google Scholar
• Pedauga, L., Velázquez, A., & Bueno, M. (2018). Property income from-whom-to-whom matrices: A dataset based on financial assets–liabilities stocks of financial instrument for Spain. Data in Brief, 19, 449–455. doi:10.1016/j.dib.2018.05.018
   PubMed Web of Science ®Google Scholar
• Perobelli, F. S., Faria, W. R., & de Almeida Vale, V. (2015). The increase in Brazilian household income and its impact on CO2 emissions: Evidence for 2003 and 2009 from input–output tables. Energy Economics, 52, 228–239. doi:10.1016/j.eneco.2015.10.007
   Web of Science ®Google Scholar
• Pyatt, G. (1988). A SAM approach to modeling. Journal of Policy Modeling, 10(3), 327–352. doi:10.1016/0161-8938(88)90026-9
   Web of Science ®Google Scholar
• Rasmussen, N. P. (1956). Studies in intersectoral relations. North Holland Publishing Co.
   Google Scholar
• Roland-Holst, D. W., & Sancho, F. (1995). Modeling prices in a SAM structure. The Review of Economics and Statistics, 77(2), 361–371. doi:10.2307/2109871
   Web of Science ®Google Scholar
• Rueda-Cantuche, J. M., Dietzenbacher, E., Fernández, E., & Amores, A. F. (2013). The bias of the multiplier matrix when supply and use tables are stochastic. Economic Systems Research, 25(4), 435–448. doi:10.1080/09535314.2013.776947
   Web of Science ®Google Scholar
• Rueda-Cantuche, J. M., Kratena, K., Streicher, G., Neuwahl, F., Mongelli, I., Rueda-Cantuche, J. M., Genty, A., Arto, I., & Andreoni, V. (2012). FIDELIO: A Fully Interregional Dynamic Econometric Long-Term Input-Output Model for the EU, EcoMod2012, N° 4082, EcoMod.
   Google Scholar
• Saldías, M. (2011). Sectoral credit risk in the euro area. Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies.
   Google Scholar
• Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. The Journal of Economic Perspectives, 15(4), 101–115. doi:10.1257/jep.15.4.101
   Web of Science ®Google Scholar
• Strassert, G. (1968). Zur bestimmung strategischer sektoren mit hilfe von input-output-modellen. Jahrbücher für Nationalökonomie und Statistik, 182(1), 211–215. doi:10.1515/jbnst-1968-0114
   Google Scholar
• Temursho, U. (2017). Uncertainty treatment in input–output analysis. In Handbook of Input–Output Analysis. Edward Elgar Publishing.
   Google Scholar
• Temurshoev, U. (2010). Identifying optimal sector groupings with the hypothetical extraction method. Journal of Regional Science, 50(4), 872–890. doi:10.1111/j.1467-9787.2010.00678.x
   Web of Science ®Google Scholar
• ten Raa, T. T., & Rueda-Cantuche, J. M. (2007). Stochastic analysis of input–output multipliers on the basis of use and make tables. Review of Income and Wealth, 53(2), 318–334. doi:10.1111/j.1475-4991.2007.00227.x
   Web of Science ®Google Scholar
• Thorbecke, E. (2000). The use of social accounting matrices in modeling. Paper presented at the 26th General conference of the international association for research in income and wealth, Krakow, Poland, August 27–September 2, 2000.
   Google Scholar
• Tissot, B. (2016). Closing information gaps at the global level – what micro data can bring, IFC Bulletin, no 41, May.
   Google Scholar
• Tsujimura, K., & Tsujimura, M. (2010). A flow-of-funds analysis of quantitative monetary policy. In S. Ichimura, & L. R. Klein (Eds.), Macroeconometric analysis of Japan (pp. 173–193). World Scientific.
   Google Scholar
• United Nations, Commission of the European Communities, International Monetary Fund, Organization for Economic Co-operation & Development and World Bank. (2008). System of National Accounts 2008. World Bank.
   Google Scholar
• van de Ven, P., & Fano, D. (2017). Understanding financial accounts. OECD Publishing.
   Google Scholar
• Welch, B. L. (1947). The generalization of student’s problem when several different population variances are involved. Biometrika, 34(1–2), 28–35. https://doi.org/10.1093/biomet/34.1-2.28
   PubMed Web of Science ®Google Scholar
• Wood, R., & Lenzen, M. (2009). Structural path decomposition. Energy Economics, 31(3), 335–341. doi:10.1016/j.eneco.2008.11.003
   Web of Science ®Google Scholar
• Zhao, Y., Wang, S., Liu, Y., Zhang, Z., Zhang, Y., & Li, H. (2017). Identifying the economic and environmental impacts of China's trade in intermediates within the Asia-pacific region. Journal of Cleaner Production, 149, 164–179. doi:10.1016/j.jclepro.2017.02.085
   Web of Science ®Google Scholar