Compound poisson models for weighted networks with applications in finance
File(s)
Author(s)
Gandy, Axel
Veraart, Luitgard AM
Type
Journal Article
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 between 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.
edge weights in weighted networks often arise from aggregating some individual relationships between 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.
Date Issued
2020-05-29
Date Acceptance
2020-05-04
Citation
Mathematics and Financial Economics, 2020, 15, pp.131-153
ISSN
1862-9660
Publisher
Springer Verlag
Start Page
131
End Page
153
Journal / Book Title
Mathematics and Financial Economics
Volume
15
Copyright Statement
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://link.springer.com/article/10.1007%2Fs11579-020-00268-9
Subjects
Social Sciences
Science & Technology
Physical Sciences
Business, Finance
Economics
Mathematics, Interdisciplinary Applications
Social Sciences, Mathematical Methods
Business & Economics
Mathematics
Mathematical Methods In Social Sciences
Weighted directed networks
Compound Poisson distribution
Regression
Subnetwork prediction
Financial networks
Systemic risk
GRAPHS
MARKET
CONTAGION
RISK
1502 Banking, Finance and Investment
Publication Status
Published
Date Publish Online
2020-05-29