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NetVIX — A network volatility index of financial markets

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  • Ahelegbey, Daniel Felix
  • Giudici, Paolo

Abstract

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market risk. The NetVIX multiplicatively decomposes into a network volatility effect and a network contagion effect. It also additively decomposes into volatility contributions of each market. 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. We show that the NetVIX has a strong relationship with the VIX index, and therefore able to reliably signal changes in global market volatility. We also show that while the NetVIX tracks to some extent the VIX, it provides much more information about the level of volatility and contagion effects in financial markets. The result shows that during crisis periods, particularly the tech bubble, the global financial crisis, and the Covid-19 pandemic, stock market interconnectedness contributes to global market turmoil by amplifying average market volatility with over 400 percent multiplier. Also during crisis times, the level of risk is relatively higher and more persistent in the US and German markets, which implies market losses for investors with long exposures. The results also reveal that the highest risk-contributing markets are the US, Brazil, Hong Kong, France, and Germany.

Suggested Citation

  • Ahelegbey, Daniel Felix & Giudici, Paolo, 2022. "NetVIX — A network volatility index of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    • Handle: RePEc:eee:phsmap:v:594:y:2022:i:c:s0378437122000917
    DOI: 10.1016/j.physa.2022.127017
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    Citations

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    Cited by:

    1. Daniel Felix Ahelegbey, 2022. "Statistical Modelling of Downside Risk Spillovers," FinTech, MDPI, vol. 1(2), pages 1-10, April.
    2. 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).
    3. 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).

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    More about this item

    Keywords

    Centrality; Covid-19; Financial crises; NetVIX; Contagion effect; VAR; VIX;
    All these 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

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