Bibinger, M., Hautsch, N., Malec , P. and Reiss, M.
Estimating the Spot Covariation of Asset Prices - Statistical Theory and Empirical Evidence
CWPE1464
Abstract: We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale
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.
Keywords: local method of moments, spot covariance, smoothing, intraday (co-)variation
risk
JEL Codes: C58 C14 C32
Author links:
PDF: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1464.pdf 
Open Access Link: https://doi.org/10.17863/CAM.5830
