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The stabilizing effect of volatility in financial markets

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  • Davide Valenti
  • Giorgio Fazio
  • Bernardo Spagnolo

Abstract

In financial markets, greater volatility is usually considered synonym of greater risk and instability. However, large market downturns and upturns are often preceded by long periods where price returns exhibit only small fluctuations. To investigate this surprising feature, here we propose using the mean first hitting time, i.e. the average time a stock return takes to undergo for the first time a large negative or positive variation, as an indicator of price stability, and relate this to a standard measure of volatility. In an empirical analysis of daily returns for $1071$ stocks traded in the New York Stock Exchange, we find that this measure of stability displays nonmonotonic behavior, with a maximum, as a function of volatility. Also, we show that the statistical properties of the empirical data can be reproduced by a nonlinear Heston model. This analysis implies that, contrary to conventional wisdom, not only high, but also low volatility values can be associated with higher instability in financial markets.

Suggested Citation

  • Davide Valenti & Giorgio Fazio & Bernardo Spagnolo, 2017. "The stabilizing effect of volatility in financial markets," Papers 1708.08695, arXiv.org.
  • Handle: RePEc:arx:papers:1708.08695
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    Cited by:

    1. G. Filatrella & N. De Liso, 2019. "Predicting one type of technological motion? A nonlinear map to study the 'sailing-ship' effect," Papers 1912.11250, arXiv.org.
    2. Leng, Na & Li, Jiang-Cheng, 2020. "Forecasting the crude oil prices based on Econophysics and Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    3. Chun Zhang & Tao Yang & Shi-Xian Qu, 2021. "Impact of time delays and environmental noise on the extinction of a population dynamics model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(11), pages 1-16, November.
    4. Venelina Nikolova & Juan E. Trinidad Segovia & Manuel Fernández-Martínez & Miguel Angel Sánchez-Granero, 2020. "A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    5. Li, Jiang-Cheng & Leng, Na & Zhong, Guang-Yan & Wei, Yu & Peng, Jia-Sheng, 2020. "Safe marginal time of crude oil price via escape problem of econophysics," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    6. Zhen, Fang, 2020. "Asymmetric signals and skewness," Economic Modelling, Elsevier, vol. 90(C), pages 32-42.
    7. Zhong, Guang-Yan & He, Feng & Li, Jiang-Cheng & Mei, Dong-Cheng & Tang, Nian-Sheng, 2019. "Coherence resonance-like and efficiency of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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