Compound poisson models for weighted networks with applications in finance

Axel Gandy and Luitgard A. M. Veraart

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships be- tween the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks.

Keywords: eighted directed networks; compound Poisson distribution; regression; subnetwork prediction; financial networks; systemic risk (search for similar items in EconPapers)
JEL-codes: C02 C46 C53 D85 G32 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2021-01-01
New Economics Papers: this item is included in nep-ecm, nep-net, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Mathematics and Financial Economics, 1, January, 2021, 15(1), pp. 131 - 153. ISSN: 1862-9679

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:104185

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