Working Paper: NBER ID: w10111
Authors: Lan Zhang; Per A. Mykland; Yacine Aït-Sahalia
Abstract: It is a common practice in finance to estimate volatility from the sum of frequently-sampled squared returns. However market microstructure poses challenges to this estimation approach, as evidenced by recent empirical studies in finance. This work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving the continuous-time assumption on the underlying returns. Under our framework, it becomes clear why and where the usual' volatility estimator fails when the returns are sampled at the highest frequency.
Keywords: No keywords provided
JEL Codes: C32; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| high-frequency data sampling (C58) | accuracy of integrated volatility estimation (C58) |
| market microstructure effects (G14) | observation error (C90) |
| sampling frequency (C83) | reliability of volatility estimates (C58) |
| traditional realized volatility estimator (C58) | magnitude of noise (C58) |
| multigrid method (C02) | improved estimation accuracy (C13) |
| sampling frequency (C83) | biased estimates of volatility (C51) |