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Realized Volatility and Absolute Return Volatility: A Comparison Indicating Market Risk

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  • Zeyu Zheng
  • Zhi Qiao
  • Tetsuya Takaishi
  • H Eugene Stanley
  • Baowen Li

Abstract

Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.

Suggested Citation

  • Zeyu Zheng & Zhi Qiao & Tetsuya Takaishi & H Eugene Stanley & Baowen Li, 2014. "Realized Volatility and Absolute Return Volatility: A Comparison Indicating Market Risk," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
  • Handle: RePEc:plo:pone00:0102940
    DOI: 10.1371/journal.pone.0102940
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    References listed on IDEAS

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    Cited by:

    1. Chiang, Thomas C. & Chen, Xiaoyu, 2016. "Stock returns and economic fundamentals in an emerging market: An empirical investigation of domestic and global market forces," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 107-120.
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    5. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M., 2020. "The (in)efficiency of NYMEX energy futures: A multifractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    6. Bourghelle, David & Jawadi, Fredj & Rozin, Philippe, 2022. "Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 294-306.
    7. Fernandes, Leonardo H.S. & Silva, José W.L. & de Araujo, Fernando H.A. & Ferreira, Paulo & Aslam, Faheem & Tabak, Benjamin Miranda, 2022. "Interplay multifractal dynamics among metal commodities and US-EPU," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    8. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2016.
    9. Dong, Yang & Wen, Shu-hui & Hu, Xiao-bing & Li, Jiang-Cheng, 2020. "Stochastic resonance of drawdown risk in energy market prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    10. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia, 2023. "Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence," International Review of Financial Analysis, Elsevier, vol. 87(C).
    11. Ji Ho Kwon, 2021. "On the factors of Bitcoin’s value at risk," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    12. Maria Elvira Mancino & Maria Cristina Recchioni, 2015. "Fourier Spot Volatility Estimator: Asymptotic Normality and Efficiency with Liquid and Illiquid High-Frequency Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-33, September.
    13. Volta, Vittoria & Aste, Tomaso, 2022. "Causal coupling between European and UK markets triggered by announcements of monetary policy decisions," LSE Research Online Documents on Economics 114947, London School of Economics and Political Science, LSE Library.
    14. Matthieu Garcin & Clément Goulet, 2015. "Non-parameteric news impact curve: a variational approach," Documents de travail du Centre d'Economie de la Sorbonne 15086rr, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Feb 2017.
    15. Persakis, Antonios & Iatridis, George Emmanuel, 2023. "How economic uncertainty influences the performance of investor perceptions and behavior," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).

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