Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/79613 
Erscheinungsjahr: 
2013
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2013-017
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high-frequency asymptotics. Our approach relies on an asymptotically equivalent continuous-time observation model where a local generalised method of moments in the spectral domain turns out to be optimal. Asymptotic semiparametric efficiency is established in the Cramér-Rao sense. Main findings are that non-synchronicity of observation times has no impact on the asymptotics and that major efficiency gains are possible under correlation. Simulations illustrate the finite-sample behaviour.
Schlagwörter: 
adaptive estimation
asymptotic equivalence
asynchronous observations
integrated covolatility matrix
quadratic covariation
semiparametric efficiency
microstructure noise
spectral estimation
JEL: 
C14
C32
C58
G10
Dokumentart: 
Working Paper

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