If efficient asset prices follow continuous semi-martingales and are perfectly observed, their quadratic variation can be measured accurately from the sum of a large number of squared returns sampled over very finely spaced intervals, i.e., realized variance (Andersen et al., 2003, and Barndorff-Nielsen and Shephard, 2002). With the emergence of high-frequency data, it seems that we should be a...

Broadband noise on supercontinuum spectra generated in microstructure fiber is shown to lead to amplitude fluctuations as large as 50% for certain input laser pulse parameters. We study this noise using both experimental measurements and numerical simulations with a generalized stochastic nonlinear Schrödinger equation, finding good quantitative agreement over a range of input-pulse energies an...

The wide availability of high-frequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jump-diffusion processes observed with market microstructure noise are frequently used to model high-frequency financial data. Yet, existing methods are developed for either noisy data from a continuous diffusion price model or data ...

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker sc...

Classical statistics suggest that for inference purposes one should always use as much data as is available. We study how the presence of market microstructure noise in high-frequency financial data can change that result. We derive the optimal sampling frequency when the observations are contaminated by market microstructure effects: empirically, it is measured in hours, not minutes. We then a...