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

Vitali Alexeev, Chen Jun () and Ignatieva Katja ()
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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

Studies in Nonlinear Dynamics & Econometrics, 2023, vol. 27, issue 5, 733-763

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.

Keywords: high-frequency data; integrated variance; pre-averaging; sampling scheme (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/snde-2021-0093

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