Inference for Continuous Semimartingales Observed at High Frequency

By Per Aslak Mykland, Lan Zhang. 2009

90 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a8e1d775989904c1fc13fb3f9eee4fdce5ed501e

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In Finance

In Management Science and Operations Research

Citations in the corpus

Quasi-maximum likelihood estimation of volatility with high frequency data (2010, background): “As argued in Newey and McFadden (1994, pp. 2133–2134) (see also Rockafellar (1970, Theorem 10.8)), the point-wise convergence of concave functions implies uniform convergence. Note that i n ̄ i n can be regarded as the difference of two concave functions from (12), then a slightly modified proof of”
On the number of common factors with high‐frequency data (2017, background): “For related work, see Mykland & Zhang (2009).”
Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading (2011, background): “This can be relaxed to locally bounded if(µ, σ ) is an Ito process — e.g. Mykland & Zhang (2009a).”
Principal Component Analysis of High-Frequency Data (2016, unknown): “An alternative inference theory in Mykland and Zhang (2009) is designed for a class of estimators based on an aggregation of local estimates using a finite number of blocks.”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “Assumption (V) is common in the realized volatility literature (see, e.g., Barndorff-Nielsen, Hansen, Lunde, and Shephard, 2008; Gon¸calves and Meddahi, 2009; Mykland and Zhang, 2009; Christensen, Podolskij, and Vetter, 2013; Hounyo, 2017).”
Do price and volatility jump together (2010, methodology): “The “local Gaussianity” of the high-frequency increments has been also used in [7] in a different context, i.”