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Uncertainty, volatility and the persistence norms of financial time series

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  • Simon Rudkin
  • Wanling Qiu
  • Pawel Dlotko

Abstract

Norms of Persistent Homology introduced in topological data analysis are seen as indicators of system instability, analogous to the changing predictability that is captured in financial market uncertainty indexes. This paper demonstrates norms from the financial markets are significant in explaining financial uncertainty, whilst macroeconomic uncertainty is only explainable by market volatility. Meanwhile, volatility is insignificant in the determination of norms when uncertainty enters the regression. Persistence norms therefore have potential as a further tool in asset pricing, and also as a means of capturing signals from financial time series beyond volatility.

Suggested Citation

  • Simon Rudkin & Wanling Qiu & Pawel Dlotko, 2021. "Uncertainty, volatility and the persistence norms of financial time series," Papers 2110.00098, arXiv.org.
    • Handle: RePEc:arx:papers:2110.00098
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    References listed on IDEAS

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    1. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    2. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    3. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    4. Altig, Dave & Baker, Scott & Barrero, Jose Maria & Bloom, Nicholas & Bunn, Philip & Chen, Scarlet & Davis, Steven J. & Leather, Julia & Meyer, Brent & Mihaylov, Emil & Mizen, Paul & Parker, Nicholas &, 2020. "Economic uncertainty before and during the COVID-19 pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    5. Katz, Yuri A. & Biem, Alain, 2021. "Time-resolved topological data analysis of market instabilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    6. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    7. Dave Altig & Scott Baker & Jose Maria Barrero & Nick Bloom & Philip Bunn & Scarlet Chen & Steven J Davis & Julia Leather & Brent Meyer & Emil Mihaylov & Paul Mizen & Nick Parker & Thomas Renault & Paw, 2020. "Economic uncertainty before and during the Covid-19 pandemic," Bank of England working papers 876, Bank of England.
    8. Bali, Turan G. & Subrahmanyam, Avanidhar & Wen, Quan, 2021. "The Macroeconomic Uncertainty Premium in the Corporate Bond Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(5), pages 1653-1678, August.
    9. Bali, Turan G. & Brown, Stephen J. & Tang, Yi, 2017. "Is economic uncertainty priced in the cross-section of stock returns?," Journal of Financial Economics, Elsevier, vol. 126(3), pages 471-489.
    10. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
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    Cited by:

    1. Samuel W. Akingbade & Marian Gidea & Matteo Manzi & Vahid Nateghi, 2023. "Why Topological Data Analysis Detects Financial Bubbles?," Papers 2304.06877, arXiv.org.
    2. Rudkin, Simon & Rudkin, Wanling & Dłotko, Paweł, 2023. "On the topology of cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 89(C).

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