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Adjustable network reconstruction with applications to CDS exposures

Author

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  • Gandy, Axel
  • Veraart, Luitgard Anna Maria

Abstract

This paper is concerned with reconstructing weighted directed networks from the total in- and out-weight of each node. This problem arises for example in the analysis of systemic risk of partially observed financial networks. Typically a wide range of networks is consistent with this partial information. We develop an empirical Bayesian methodology that can be adjusted such that the resulting networks are consistent with the observations and satisfy certain desired global topological properties such as a given mean density, extending the approach by Gandy and Veraart (2017). Furthermore we propose a new fitness-based model within this framework. We provide a case study based on a data set consisting of 89 fully observed financial networks of credit default swap exposures. We reconstruct those networks based on only partial information using the newly proposed as well as existing methods. To assess the quality of the reconstruction, we use a wide range of criteria, including measures on how well the degree distribution can be captured and higher order measures of systemic risk. We find that the empirical Bayesian approach performs best.

Suggested Citation

  • Gandy, Axel & Veraart, Luitgard Anna Maria, 2019. "Adjustable network reconstruction with applications to CDS exposures," Journal of Multivariate Analysis, Elsevier, vol. 172(C), pages 193-209.
    • Handle: RePEc:eee:jmvana:v:172:y:2019:i:c:p:193-209
    DOI: 10.1016/j.jmva.2018.08.011
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    Citations

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    Cited by:

    1. Luitgard Anna Maria Veraart, 2020. "Distress and default contagion in financial networks," Mathematical Finance, Wiley Blackwell, vol. 30(3), pages 705-737, July.
    2. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2020. "Reconstructing and stress testing credit networks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    3. Pang, Raymond Ka-Kay & Veraart, Luitgard A. M., 2023. "Assessing and mitigating fire sales risk under partial information," LSE Research Online Documents on Economics 120171, London School of Economics and Political Science, LSE Library.
    4. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    5. Silvia Crafa, 2021. "From agent-based modeling to actor-based reactive systems in the analysis of financial networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 649-673, July.
    6. Arreola Hernandez, Jose & Kang, Sang Hoon & Shahzad, Syed Jawad Hussain & Yoon, Seong-Min, 2020. "Spillovers and diversification potential of bank equity returns from developed and emerging America," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Gandy, Axel & Veraart, Luitgard A. M., 2021. "Compound poisson models for weighted networks with applications in finance," LSE Research Online Documents on Economics 104185, London School of Economics and Political Science, LSE Library.
    8. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 678-696, January.
    9. Chen, Yu & Jin, Shuyue & Wang, Xiasi, 2021. "Solvency contagion risk in the Chinese commercial banks’ network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    10. Chao, Wang & Jing, Ma & Xiaoxing, Liu, 2023. "Optimizing systemic risk through credit network reconstruction," Emerging Markets Review, Elsevier, vol. 57(C).
    11. Amini, Hamed & Feinstein, Zachary, 2023. "Optimal network compression," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1439-1455.
    12. Hamed Amini & Zachary Feinstein, 2020. "Optimal Network Compression," Papers 2008.08733, arXiv.org, revised Jul 2022.
    13. Lukas Gonon & Thilo Meyer-Brandis & Niklas Weber, 2024. "Computing Systemic Risk Measures with Graph Neural Networks," Papers 2410.07222, arXiv.org.
    14. Jose Arreola Hernandez & Sang Hoon Kang & Ron P. McIver & Seong-Min Yoon, 2021. "Network Interdependence and Optimization of Bank Portfolios from Developed and Emerging Asia Pacific Countries," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 613-647, December.
    15. Pang, Raymond Ka-Kay & Veraart, Luitgard Anna Maria, 2023. "Assessing and mitigating fire sales risk under partial information," Journal of Banking & Finance, Elsevier, vol. 155(C).
    16. Veraart, Luitgard A. M., 2020. "Distress and default contagion in financial networks," LSE Research Online Documents on Economics 101905, London School of Economics and Political Science, LSE Library.

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