The availability of high frequency financial data has generated a series of estimators based on intra‐day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated.
MLA
Mykland, Per A., and Lan Zhang. “Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance.” Econometrica, vol. 85, .no 1, Econometric Society, 2017, pp. 197-231, https://doi.org/10.3982/ECTA12501
Chicago
Mykland, Per A., and Lan Zhang. “Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance.” Econometrica, 85, .no 1, (Econometric Society: 2017), 197-231. https://doi.org/10.3982/ECTA12501
APA
Mykland, P. A., & Zhang, L. (2017). Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance. Econometrica, 85(1), 197-231. https://doi.org/10.3982/ECTA12501
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