Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102698
Authors: 
Bibinger, Markus
Hautsch, Nikolaus
Malec, Peter
Reiss, Markus
Year of Publication: 
2014
Series/Report no.: 
CFS Working Paper Series 477
Abstract: 
We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semi-martingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
Subjects: 
local method of moments
spot covariance
smoothing
intraday (co-)variation risk
JEL: 
C58
C14
C32
Document Type: 
Working Paper

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