Abstract
This paper investigates the challenging problem of volatility estimation using high-frequency financial data when both price discreteness and structural random market microstructure noise, i.e., a known function of trading information and a finite-dimensional parameter vector, are present. Under this setup, we propose a novel approach that features both nonlinear least squares and particle filters for the estimation of integrated volatility. Theoretical underpinnings are provided justifying the effectiveness of our method. Both simulation and empirical results show the clear advantage of our approach over a number of existing methods.
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