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Capturing Financial Volatility Through Simple Network Measures

  • Pedro C. Souto
  • Andreia Sofia Teixeira
  • Alexandre P. Francisco
  • Francisco C. Santos
Conference paper
Part of the Studies in Computational Intelligence book series (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.

Keywords

Financial complex networks Financial volatility Structural balance 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro C. Souto
    • 1
    • 2
    • 3
  • Andreia Sofia Teixeira
    • 1
    • 2
    • 4
  • Alexandre P. Francisco
    • 1
  • Francisco C. Santos
    • 1
    • 2
  1. 1.INESC-ID and Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.ATP-GroupIST-TagusparkPorto SalvoPortugal
  3. 3.NOVA School of Business and Economics, Universidade Nova de LisboaLisbonPortugal
  4. 4.LASIGE, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal

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