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On the distribution of links in financial networks: Structural heterogeneity and functional form

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  • Lux, Thomas
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    We investigate the distribution of links in three large data-sets, one of these covering interbank loans in the electronic trading platform e-MID, the other two covering a large part of the loans of banks to non-financial companies in the Spanish and Japanese economies, respectively. In contrast to all previous literature, we do not assume homogeneity of the link distribution over time and across different categories of agents (banks, firms) but apply our hypothesized distributions as regression models. As it turns out, many of the tested sources of heterogeneity turn out to be significant regressors. For instance, we find pervasive time heterogeneity of link formation in all three data sets, and also heterogeneity for different categories of banks/firms that can be identified in the data. Across all networks, the Negative Binominal model always outperforms all alternative models confirming its good performance as a model of economic count data in many previous applications.

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    File URL: https://www.econstor.eu/bitstream/10419/162397/1/890592586.pdf
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    Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2017-05.

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    Date of creation: 2017
    Handle: RePEc:zbw:cauewp:201705
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    1. David C. Schmittlein & Albert C. Bemmaor & Donald G. Morrison, 1985. "Technical Note—Why Does the NBD Model Work? Robustness in Representing Product Purchases, Brand Purchases and Imperfectly Recorded Purchases," Marketing Science, INFORMS, vol. 4(3), pages 255-266.
    2. Daniel Fricke & Thomas Lux, 2015. "On the distribution of links in the interbank network: evidence from the e-MID overnight money market," Empirical Economics, Springer, vol. 49(4), pages 1463-1495, December.
    3. G. De Masi & M. Gallegati, 2012. "Bank–firms topology in Italy," Empirical Economics, Springer, vol. 43(2), pages 851-866, October.
    4. de Masi, G. & Iori, G. & Caldarelli, G., 2006. "A fitness model for the Italian interbank money market," Working Papers 06/08, Department of Economics, City University London.
    5. Anand, Kartik & Gai, Prasanna & Kapadia, Sujit & Brennan, Simon & Willison, Matthew, 2013. "A network model of financial system resilience," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 219-235.
    6. Manuel Illueca & Lars Norden & Gregory F. Udell, 2014. "Liberalization and Risk-Taking: Evidence from Government-Controlled Banks," Review of Finance, European Finance Association, vol. 18(4), pages 1217-1257.
    7. Nier, Erlend & Yang, Jing & Yorulmazer, Tanju & Alentorn, Amadeo, 2007. "Network models and financial stability," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 2033-2060, June.
    8. Karl Finger & Daniel Fricke & Thomas Lux, 2013. "Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes," Computational Management Science, Springer, vol. 10(2), pages 187-211, June.
    9. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    10. Lux, Thomas, 2016. "A model of the topology of the bank – firm credit network and its role as channel of contagion," Journal of Economic Dynamics and Control, Elsevier, vol. 66(C), pages 36-53.
    11. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.
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