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Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence. (2014). Malec, Peter ; Hautsch, Nikolaus ; Bibinger, Markus ; Reiss, Markus .
In: SFB 649 Discussion Papers.
RePEc:hum:wpaper:sfb649dp2014-055.

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  1. Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data. (2020). Wang, Weining ; Mustafayeva, Konul.
    In: IRTG 1792 Discussion Papers.
    RePEc:zbw:irtgdp:2020025.

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  2. A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics. (2019). Lillo, Fabrizio ; Corsi, Fulvio ; Bormetti, Giacomo ; Buccheri, Giuseppe .
    In: Papers.
    RePEc:arx:papers:1803.04894.

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  3. Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book. (2018). Winkelmann, Lars ; Neely, Christopher ; Bibinger, Markus.
    In: IRTG 1792 Discussion Papers.
    RePEc:zbw:irtgdp:2018055.

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  4. Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book. (2017). Neely, Christopher ; Winkelmann, Lars ; Bibinger, Markus.
    In: Working Papers.
    RePEc:fip:fedlwp:2017-012.

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  5. Microstructure noise in the continuous case: Approximate efficiency of the adaptive pre-averaging method. (2015). Jacod, Jean ; Mykland, Per A..
    In: Stochastic Processes and their Applications.
    RePEc:eee:spapps:v:125:y:2015:i:8:p:2910-2936.

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  1. (2014). There, the asymptotic theory is pursued for continuous observations, but, once we have the illustration above for I−1 k , the analysis is the same. Using block-wise transformations which diagonalize Σkhn and transfer the noise level (5) to the identity matrix, i.e., Λkhn = OkHkhn Σkhn Hkhn O> k , with Ok being orthogonal matrices and Λkhn being diagonal, we can infer the asymptotic form via COV vec ˆ Σor s = bsh−1 n c+Kn X k=bsh−1 n c−Kn (2Kn + 1)−2 OkH−1 k −⊗2 ˜ I−1 k H−1 k O> k −⊗2 Z
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  2. + O(K−1 n ), with a diagonalized version ˜ Ik of Ik. Along the same lines as in the proof of Corollary 4.3 in Bibinger et al. (2014), we derive that COV vec ˆ Σor s = (2 + O(1)) bsh−1 n c+Kn X k=bsh−1 n c−Kn (2Kn + 1)−2 × Σkhn ⊗ Σkhn H 1/2 + Σkhn H 1/2 ⊗ Σkhn
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  28. For the order of the weights we have by Lemma C.1 of Bibinger et al. (2014) uniformly over all k that kWj Hn k , Σkhn k . (log (n))−1 1 + j2 (nh2 n)−1 −2 . (27) We introduce the short notation t (p) i = (1/2) t (p) i + t (p) i−1
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  48. The two-factor model introduced by Chernov et al. (2003) allows for more pronounced movements in the instantaneous volatility by a feedback mechanism. The corresponding parameterization is ˜ σt = s–exp(β0 + β1v1,t + β2v2,t), (41) dv1,t = α1v1,tdt+dW1,t, dv2,t = α2v2,tdt + (1 + βvv2,t) dW2,t, s–exp(u) = ( exp(u) if u ≤ u0 exp(u0) p 1 − u0 + u2/u0 else, where W1,t and W2,t are standard Brownian motions with Corr(dW1,t, dBt) = ρ1 and Corr(dW2,t, dBt) = ρ2. We consider the configuration β0 = −1.2, β1 = 0.04, β2 = 1.5, α1 = −0.137e−2, α2 = −1.386, βv = 0.25, ρ1 = ρ2 = −0.3 and u0 = ln (1.5). C Tables and Figures
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  51. ZU, Y. AND P. H. BOSWIJK (2014): “Estimating Spot Volatility with High-Frequency Financial Data,” Journal of Econometrics, forthcoming. A Proofs A.1 Preliminaries Consider the process ˜ Xt = Z t σbsh−1 n chn dBs , (25) without drift and with block-wise constant volatility as a simplified approximation of X. In the following, we distinguish between the estimator of the spot covariance matrix (12) based on oracle optimal weights (13), ˆ Σor s , and the adaptive estimator ˆ Σs. Furthermore, we write ˆ Σs( ˜ X + ) for the estimator built from observations in the simplified model in which ˜ X is observed in noise and denote the associated spectral statistics by: ˜ Sjk = πjh−1 n np X i=1 ˜ X (p) t (p) i + (p) i − ˜ X (p) t (p) i−1 − (p) i−1 !

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    RePEc:eee:econom:v:180:y:2014:i:2:p:217-232.

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  41. Covariance estimation using high-frequency data: Sensitivities of estimation methods. (2014). Haugom, Erik ; Veka, Steinar ; Westgaard, Sjur ; Lien, Gudbrand.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:43:y:2014:i:c:p:416-425.

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  42. Disentangled jump-robust realized covariances and correlations with non-synchronous prices. (2014). Elst, Harry Vander ; Veredas, David.
    In: DES - Working Papers. Statistics and Econometrics. WS.
    RePEc:cte:wsrepe:ws142416.

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  43. Disentangled jump-robust realized covariances and correlations with non-synchronous prices. (2014). Veredas, David ; Elst, Harry Vander.
    In: DES - Working Papers. Statistics and Econometrics. WS.
    RePEc:cte:wsrepe:es142416.

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  44. Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading. (2014). Hounyo, Ulrich.
    In: CREATES Research Papers.
    RePEc:aah:create:2014-35.

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  45. Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity. (2014). Quaedvlieg, Rogier ; Laurent, Sébastien ; Boudt, Kris ; Lunde, Asger.
    In: CREATES Research Papers.
    RePEc:aah:create:2014-05.

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  46. The leverage effect puzzle: Disentangling sources of bias at high frequency. (2013). Fan, Jianqing ; Ait-Sahalia, Yacine.
    In: Journal of Financial Economics.
    RePEc:eee:jfinec:v:109:y:2013:i:1:p:224-249.

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  47. Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation. (2012). Corsi, Fulvio ; Audrino, Francesco ; Peluso, Stefano.
    In: Economics Working Paper Series.
    RePEc:usg:econwp:2012:02.

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  48. An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory. (2012). Bibinger, Markus.
    In: Stochastic Processes and their Applications.
    RePEc:eee:spapps:v:122:y:2012:i:6:p:2411-2453.

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  49. Testing for co-jumps in high-frequency financial data: an approach based on first-high-low-last prices. (2011). Liao, Yin ; Anderson, Heather.
    In: Monash Econometrics and Business Statistics Working Papers.
    RePEc:msh:ebswps:2011-9.

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