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Integrated variance of irregularly spaced high-frequency data: A state space approach based on pre-averaging

Author

Listed:
  • Alexeev Vitali

    (Finance Discipline Group, UTS Business School, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Chen Jun

    (School of Risk and Actuarial Studies, Business School and UNSW Data Science Hub, UNSW Sydney, Sydney, NSW 2052, Australia)

  • Ignatieva Katja

    (School of Risk and Actuarial Studies, Business School and UNSW Data Science Hub, UNSW Sydney, Sydney, NSW 2052, Australia)

Abstract

We propose a new state space model to estimate the Integrated Variance (IV) in the presence of microstructure noise. Applying the pre-averaging sampling scheme to the irregularly spaced high-frequency data, we derive equidistant efficient price approximations to calculate the noise-contaminated realised variance (NCRV), which is used as an IV estimator. The theoretical properties of the new volatility estimator are illustrated and compared with those of the realised volatility. We highlight the robustness of the new estimator to market microstructure noise (MMN). The pre-averaging sampling effectively eliminates the influence of the MMN component on the NCRV series. The empirical illustration features the EUR/USD exchange rate and provides evidence of a superior performance in volatility forecasting at very high sampling frequencies.

Suggested Citation

  • Alexeev Vitali & Chen Jun & Ignatieva Katja, 2023. "Integrated variance of irregularly spaced high-frequency data: A state space approach based on pre-averaging," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 733-763, December.
    • Handle: RePEc:bpj:sndecm:v:27:y:2023:i:5:p:733-763:n:1
    DOI: 10.1515/snde-2021-0093
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