Imperial College London
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Professor Axel Gandy

Faculty of Natural Sciences, Department of Mathematics

Head of Department of Mathematics & Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8518a.gandy Website

 
 
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Location

 

644Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gandy:2020:10.1007/s11579-020-00268-9,
author = {Gandy, A and Veraart, LAM},
doi = {10.1007/s11579-020-00268-9},
journal = {Mathematics and Financial Economics},
pages = {131--153},
title = {Compound poisson models for weighted networks with applications in finance},
url = {http://dx.doi.org/10.1007/s11579-020-00268-9},
volume = {15},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We develop a modelling framework for estimating and predicting weighted network data. Theedge weights in weighted networks often arise from aggregating some individual relationships between the nodes. Motivated by this, we introduce a modelling framework for weighted networksbased on the compound Poisson distribution. To allow for heterogeneity between the nodes, weuse a regression approach for the model parameters. We test the new modelling framework on twotypes of financial networks: a network of financial institutions in which the edge weights representexposures from trading Credit Default Swaps and a network of countries in which the edge weightsrepresent cross-border lending. The compound Poisson Gamma distributions with regression fit thedata well in both situations. We illustrate how this modelling framework can be used for predictingunobserved edges and their weights in an only partially observed network. This is for examplerelevant for assessing systemic risk in financial networks.
AU  - Gandy,A
AU  - Veraart,LAM
DO  - 10.1007/s11579-020-00268-9
EP  - 153
PY  - 2020///
SN  - 1862-9660
SP  - 131
TI  - Compound poisson models for weighted networks with applications in finance
T2  - Mathematics and Financial Economics
UR  - http://dx.doi.org/10.1007/s11579-020-00268-9
UR  - https://link.springer.com/article/10.1007%2Fs11579-020-00268-9
UR  - http://hdl.handle.net/10044/1/79197
VL  - 15
ER  -
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