How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise
Yacine Ait-Sahalia and
Per A. Mykland
No 9611, NBER Working Papers from National Bureau of Economic Research, Inc
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 show that the optimal sampling frequency at which to estimate the parameters of a discretely sampled continuous-time model can be finite when the observations are contaminated by market microstructure effects. We then address the question of what to do about the presence of the noise. We show that modelling the noise term explicitly restores the first order statistical effect that sampling as often as possible is optimal. But, more surprisingly, we also demonstrate that this is true even if one misspecifies the assumed distribution of the noise term. Not only is it still optimal to sample as often as possible, but the estimator has the same variance as if the noise distribution had been correctly specified, implying that attempts to incorporate the noise into the analysis cannot do more harm than good. Finally, we study the same questions when the observations are sampled at random time intervals, which are an essential feature of transaction-level data.
JEL-codes: G12 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-fmk and nep-rmg
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Published as Review of Financial Studies, 2005, vol. 18, pp. 351-416
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Journal Article: How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise (2005)
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