TY - UNPD A1 - Bibinger, Markus A1 - Hautsch, Nikolaus A1 - Malec, Peter A1 - Reiß, Markus T1 - Estimating the spot covariation of asset prices – statistical theory and empirical evidence T2 - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 477 N2 - 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. T3 - CFS working paper series - 477 KW - local method of moments KW - spot covariance KW - smoothing KW - intraday (co-)variation risk Y1 - 2014 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35109 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-351099 UR - http://ssrn.com/abstract=2507714 IS - October 2014 PB - Center for Financial Studies CY - Frankfurt, M. ER -