IDEAS home Printed from https://ideas.repec.org/p/pav/demwpp/demwp0192.html

NetVIX - A Network Volatility Index of Financial Markets

Author

Listed:
  • Daniel Felix Ahelegbey

    (University of Pavia)

  • Paolo Giudici

    (University of Pavia)

Abstract

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market turbulence. The NetVIX multiplicatively decomposes into an average volatility and a network amplifier index. It also additively decomposes into marginal volatility indices for measuring individual contribution to global turmoil. We apply our measure to study the relationship between the interconnectedness among 20 major stock markets and global market risks over the last two decades. The NetVIX is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. The result shows that during crisis periods, particularly the tech-bubble, sub-prime, and COVID-19 pandemic, the interconnectedness of the markets amplifies average market risk more than 700 percent to cause a global meltdown. We find evidence that the highest risk-contributing markets to global meltdown are the US, Brazil, Hong Kong, France, and Germany.

Suggested Citation

  • Daniel Felix Ahelegbey & Paolo Giudici, 2020. "NetVIX - A Network Volatility Index of Financial Markets," DEM Working Papers Series 192, University of Pavia, Department of Economics and Management.
    • Handle: RePEc:pav:demwpp:demwp0192
    as

    Download full text from publisher

    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0192.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Palomba, Giulio & Tedeschi, Marco, 2024. "Contagion among European financial indices, evidence from a quantile VAR approach," Economic Systems, Elsevier, vol. 48(2).
    2. Daniel Felix Ahelegbey, 2022. "Statistical Modelling of Downside Risk Spillovers," FinTech, MDPI, vol. 1(2), pages 1-10, April.
    3. Celani, Alessandro & Cerchiello, Paola & Pagnottoni, Paolo, 2024. "The topological structure of panel variance decomposition networks," Journal of Financial Stability, Elsevier, vol. 71(C).
    4. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    5. Zheng, Yanting & Luan, Xin & Lu, Xin & Liu, Jiaming, 2023. "A new view of risk contagion by decomposition of dependence structure: Empirical analysis of Sino-US stock markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    6. Levantesi, Susanna & Piscopo, Gabriella & Roviello, Alba, 2025. "Cryptocurrency in global dynamics: Analyzing the Crypto Volatility Index and financial markets with machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
    7. Ahelegbey, Daniel Felix & Celani, Alessandro & Cerchiello, Paola, 2024. "Measuring the impact of the EU health emergency response authority on the economic sectors and the public sentiment," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    8. Lv, Jiamin & Ben, Shenglin & Huang, Wenli & Xu, Yueling, 2023. "How to reduce the default contagion risk of intercorporate credit guarantee networks? Evidence from China," Emerging Markets Review, Elsevier, vol. 55(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pav:demwpp:demwp0192. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alice Albonico The email address of this maintainer does not seem to be valid anymore. Please ask Alice Albonico to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/dppavit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.