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Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality

Author

Listed:
  • Thibault Vatter

    (Faculty of Business and Economics (HEC), University of Lausanne, 1015 Lausanne, Switzerland)

  • Hau-Tieng Wu

    (Department of Mathematics, University of Toronto, Toronto M5S2E4, ON, Canada)

  • Valérie Chavez-Demoulin

    (Faculty of Business and Economics (HEC), University of Lausanne, 1015 Lausanne, Switzerland)

  • Bin Yu

    (Department of Statistics, University of California, Berkeley 94720, CA, USA)

Abstract

We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically time-varying and evolve in real time. We provide the associated estimators and use simulations to show that they behave adequately in the presence of jumps and heteroskedastic and heavy-tailed noise. A study of exchange rate returns sampled from 2010 to 2013 suggests that failing to factor in the seasonality’s dynamic properties may lead to misestimation of the intraday spot volatility.

Suggested Citation

  • Thibault Vatter & Hau-Tieng Wu & Valérie Chavez-Demoulin & Bin Yu, 2015. "Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality," Econometrics, MDPI, vol. 3(4), pages 1-24, December.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:4:p:864-887:d:60871
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    References listed on IDEAS

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    2. Naeem, Muhammad & Shahbaz, Muhammad & Saleem, Kashif & Mustafa, Faisal, 2019. "Risk analysis of high frequency precious metals returns by using long memory model," Resources Policy, Elsevier, vol. 61(C), pages 399-409.

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