Skip to main content
SpringerLink
Log in
Book cover

International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2018: Complex Networks and Their Applications VII pp 534–546Cite as

Capturing Financial Volatility Through Simple Network Measures

  • Conference paper
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 813))

Abstract

Measuring the inner characteristics of financial markets risks have been proven to be key at understanding what promotes financial instability and volatility swings. Advances in complex network analysis have shown the capability to characterize the specificities of financial networks, ranging from credit networks, volatility networks, and supply-chain networks, among other examples. Here, we present a price-correlation network model in which Standard & Poors’ members are nodes connected by edges corresponding to price-correlations over time. We use the average degree and the frequency of specific motifs, based on structural balance, to evaluate if it is possible, with these simple measures, to identify financial volatility. Our results suggest the existence of a significant correlation between the Index implied volatility (measured with the VIX Index) and the average degree of the network. Moreover, we identify a close relation between volatility and the number of balanced positive triads. These results are shown to be robust to a wide range of time windows and correlations thresholds, suggesting that market instability can be inferred from simple topological features.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Which is one of the most accurate database when we are dealing with financial data. The dataset has, on average, 252 days per year during 26 years.

  2. 2.

    http://www.dcc.fc.up.pt/gtries/.

References

  1. Afonso, G., Kovner, A., Schoar, A.: Trading partners in the interbank lending market (2013)

    Google Scholar 

  2. Allen, F., Babus, A.: Networks in finance. The Network Challenge, pp. 367–382 (2009)

    Google Scholar 

  3. Allen, F., Babus, A., Carletti, E.: Asset commonality, debt maturity and systemic risk. J. Financ. Econ. 104(3), 519–534 (2012)

    Article  Google Scholar 

  4. Bardoscia, M., Battiston, S., Caccioli, F., Caldarelli, G.: Debtrank: a microscopic foundation for shock propagation. PloS one 10(6), e0130406 (2015)

    Article  Google Scholar 

  5. Bardoscia, M., Battiston, S., Caccioli, F., Caldarelli, G.: Pathways towards instability in financial networks. Nat. Commun. 8, 14416 (2017)

    Article  Google Scholar 

  6. Bardoscia, M., Caccioli, F., Perotti, J.I., Vivaldo, G., Caldarelli, G.: Distress propagation in complex networks: the case of non-linear debtrank. PloS one 11(10), e0163825 (2016)

    Article  Google Scholar 

  7. Barucca, P., Bardoscia, M., Caccioli, F., D’Errico, M., Visentin, G., Battiston, S., Caldarelli, G.: Network valuation in financial systems (2016)

    Google Scholar 

  8. Battiston, S., Caldarelli, G., DErrico, M., Gurciullo, S.: Leveraging the network: a stress-test framework based on debtrank. Stat. Risk Model. 33(3–4), 117–138 (2016)

    Google Scholar 

  9. Battiston, S., Caldarelli, G., May, R.M., Roukny, T., Stiglitz, J.E.: The price of complexity in financial networks. Proc. Natl. Acad. Sci. 113(36), 10031–10036 (2016)

    Article  MathSciNet  Google Scholar 

  10. Battiston, S., Puliga, M., Kaushik, R., Tasca, P., Caldarelli, G.: Debtrank: Too central to fail? financial networks, the fed and systemic risk. Sci. Rep. 2, 541 (2012)

    Article  Google Scholar 

  11. Bodie, Z., Kane, A., Marcus, A.J.: Investment and Portfolio Management. McGraw-Hill Irwin (2011)

    Google Scholar 

  12. Boss, M., Elsinger, H., Summer, M., Thurner 4, S.: Network topology of the interbank market. Quant. Financ. 4(6), 677–684 (2004)

    Google Scholar 

  13. Cartwright, D., Harary, F.: Structural balance: a generalization of Heider’s theory (1956)

    Google Scholar 

  14. Davis, J.A.: Clustering and structural balance in graphs. Hum. Relat. 20(2), 181–187 (1967)

    Article  Google Scholar 

  15. Exchange, C.B.O.: The cboe volatility index–vix. White Paper (2009)

    Google Scholar 

  16. Fama, E.F.: Efficient capital markets: Ii. J. Financ. 46(5), 1575–1617 (1991)

    Article  Google Scholar 

  17. Griffin, K.: Testimony to the house committee on oversight and government reform. Prepared for the US House of Representatives, Committee on Oversight and Government Reform, Hearing on Hedge Funds, Washington, DC, 13 Nov 2008

    Google Scholar 

  18. Harary, F.: On the notion of balance of a signed graph. Mich. Math. J. 2(2), 143–146 (1953)

    Article  MathSciNet  Google Scholar 

  19. Leonidov, A., Rumyantsev, E.: Russian interbank networks: main characteristics and stability with respect to contagion (2012). arXiv:1210.3814

  20. Lo, A.: Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press, Princeton (2017)

    Google Scholar 

  21. Lo, A.W.: The adaptive markets hypothesis. J. Portf. Manag. 30(5), 15–29 (2004)

    Article  Google Scholar 

  22. Onnela, J.P., Kaski, K., Kertész, J.: Clustering and information in correlation based financial networks. Eur. Phys. J. B 38(2), 353–362 (2004)

    Article  Google Scholar 

  23. Onnela, J.P., Saramäki, J., Kertész, J., Kaski, K.: Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71(6), 065,103 (2005)

    Article  Google Scholar 

  24. Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted network. Proc. Natl. Acad. Sci. 101, 3747–3752 (2004)

    Article  Google Scholar 

  25. Ribeiro, P., Silva, F.: Querying subgraph sets with g-tries. In: Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2012, pp. 25–30. ACM, New York (2012). https://doi.org/10.1145/2304536.2304541

  26. e Santos, E.B., Cont, R., et al.: The brazilian interbank network structure and systemic risk. Technical Report (2010)

    Google Scholar 

  27. Souto, P.: Marketopolis: a market simulation based on investors decisions. Master’s thesis (2016)

    Google Scholar 

  28. Visentin, G., Battiston, S., D’Errico, M.: Rethinking financial contagion (2016)

    Google Scholar 

  29. Wyman, O.: Managing complexity: the state of the financial services industry 2015 (2015)

    Google Scholar 

Download references

Acknowledgments

This work was partly supported by national funds through Universidade de Lisboa and FCT –Fundação para a Ciência e Tecnologia, under projects SFRH/BD/129072/2017, PTDC/EEI-SII/5081/2014, PTDC/MAT/STA/3358/2014, and UID/CEC/50021/2013. We are grateful to Bruno Gonçalves for comments.

Author information

Authors and Affiliations

  1. INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Pedro C. Souto, Andreia Sofia Teixeira, Alexandre P. Francisco & Francisco C. Santos

  2. ATP-Group, IST-Taguspark, Porto Salvo, Portugal

    Pedro C. Souto, Andreia Sofia Teixeira & Francisco C. Santos

  3. NOVA School of Business and Economics, Universidade Nova de Lisboa, Lisbon, Portugal

    Pedro C. Souto

  4. LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

    Andreia Sofia Teixeira

Authors
  1. Pedro C. Souto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreia Sofia Teixeira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandre P. Francisco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco C. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreia Sofia Teixeira .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Souto, P.C., Teixeira, A.S., Francisco, A.P., Santos, F.C. (2019). Capturing Financial Volatility Through Simple Network Measures. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_43

Download citation

Publish with us

Policies and ethics

Search

Navigation