Realized volatility with stochastic sampling
By Masaaki Fukasawa. 2010
54 citations recorded by the source corpus.
A central limit theorem for the realized volatility of a one-dimensional continuous semimartingale based on a general stochastic sampling scheme is proved. The asymptotic distribution depends on the sampling scheme, which is written explicitly in terms of the asymptotic skewness and kurtosis of returns. Conditions for the central limit theorem to hold are examined for several concrete examples of schemes. Lower bounds for mean squared error and for asymptotic conditional variance are given, which are attained by using a specific sampling scheme.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/e96a85cb6be479b0ab6cfb942f249ce2e9f37384
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In Economics and Econometrics
- Markus Bibinger
- Yingying Li
- Per Aslak Mykland
- Xinghua Zheng
- Lan Zhang
- É. Renault
- Yacine Aït-Sahalia
- Asger Lunde
In Finance
- Markus Bibinger
- Yingying Li
- Per Aslak Mykland
- Lan Zhang
- Xinghua Zheng
- Yacine Aït-Sahalia
- É. Renault
- Asger Lunde
Citations in the corpus
- REALIZED VOLATILITY WHEN SAMPLING TIMES ARE POSSIBLY ENDOGENOUS (2013, methodology): “The same applies to the result of Hayashi, Jacod, and Yoshida (2008) and Phillips and Yu (2007).”
- An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory (2011, background): “For other applications and expositions of the theory from [18] we refer to [24], [12] and [7].”