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Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices

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  • Georgia Zournatzidou

    (Department of Accounting and Finance, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Christos Floros

    (Department of Accounting and Finance, Hellenic Mediterranean University, 71410 Heraklion, Greece)

Abstract

In this study, we analyze the volatility of volatility indices and estimate the Hurst parameter using data from five international markets. For our analysis, we consider daily data from VIX (CBOE), VXN (CBOE Nasdaq 100), VXD (DJIA), VHSI (HSI), and KSVKOSPI (KOSPI). The period of analysis is from January 2001 to December 2021 and incorporates various market phases, such as booms and crashes. The novelty here is the use of recent methodology, including different range-based estimators for volatility analysis. We apply the Hurst exponent to the volatility measures V gk , t , V p , t , V rs , t , and V s , t , and then estimate the volatility of volatility indices through the GARCH(1, 1) model. Based on the values of the Hurst exponent, we analyze the trace of the behavior of three trading strategies, i.e., the momentum-based strategy, the random walk, and the mean-reversion strategy. The results are highly recommended for financial analysts dealing with volatility indices as well as for financial researchers.

Suggested Citation

  • Georgia Zournatzidou & Christos Floros, 2023. "Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices," JRFM, MDPI, vol. 16(5), pages 1-15, May.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:5:p:272-:d:1147387
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    References listed on IDEAS

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    3. Hwang, Soosung & Satchell, Stephen E., 2000. "Market risk and the concept of fundamental volatility: Measuring volatility across asset and derivative markets and testing for the impact of derivatives markets on financial markets," Journal of Banking & Finance, Elsevier, vol. 24(5), pages 759-785, May.
    4. Seyed Alireza Athari & Ngo Thai Hung, 2022. "Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(4), pages 736-756, October.
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    Cited by:

    1. Juraj Smiesko & Pavel Segec & Martin Kontsek, 2023. "Machine Recognition of DDoS Attacks Using Statistical Parameters," Mathematics, MDPI, vol. 12(1), pages 1-30, December.
    2. Georgia Zournatzidou & Dimitrios Farazakis & Ioannis Mallidis & Christos Floros, 2024. "Stochastic Patterns of Bitcoin Volatility: Evidence across Measures," Mathematics, MDPI, vol. 12(11), pages 1-16, May.

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