Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise

By Ole E. Barndorff-Nielsen, P. R. Hansen, Asger Lunde, N. Shephard. 2008

1,353 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/903aadac8aaa28a897b9f7be1d674ce7c4285d2e

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Citations in the corpus

Realised quantile-based estimation of the integrated variance (2010, methodology): “Consequently, the lower bound for the variance of QRV∗ is also 8.5σ 3ω (note that for a suitable choice of parameters the realised kernel of Barndorff-Nielsen et al. (2008) can attain a variance of 8.002σ 3ω).”
Speculators, Prices and Market Volatility (2016, result): “…realized volatility estimator corrects for the bias of the noise by subtracting the noise variance from the average estimator 9 Alternatively, the Barndorff-Nielsen et al. (2008) kernel estimator and the Andersen et al. (2001) low frequency sampling approach yield qualitatively similar results.”
Asymptotic equivalence for inference on the volatility from noisy observations (2011, background): “Mainly three different, but closely related approaches have been proposed afterwards to estimate the integrated volatility: multi-scale estimators [Zhang (2006)], realized kernels or autocovariances [Barndorff-Nielsen et al. (2008)] and preaveraging [Jacod et al. (2009)]. Under various degrees of ge”
Quasi-maximum likelihood estimation of volatility with high frequency data (2010, background): “Recently, Barndorff-Nielsen et al. (2008) have designed various Realized Kernels (RKs) that can be used to deal with endogenous noise and endogenously spaced data and their convergence rates are the same as that of the MSRV.”
Data-based ranking of realised volatility estimators (2011, methodology): “Xit = QVt + it, i = 1; 2 (19) 1t = ! 30min t + (1 !) uU1t (20) 2t = ! 30min t + (1 !) uU2t + q 2 2 2 1U3t (21) [U1t; U2t; U3t] 0 s iid N (0; I) where 30min t RV 30min t IVt The above structure allows the measurement error on each of the RV estimators to be correlated with the proxy measurement error”
On the number of common factors with high‐frequency data (2017, background): “Barndorff-Nielsen et al. (2008) and Fan et al. (2012) used (2) to mimic the real log-prices in their simulation studies.”
Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading (2011, methodology): “wherea is a vector of elements which are predictable locally bounde d rifts, σ is a càdlàg volatility matrix process andW is a vector of independent Brownian motions. For reviews of t he econometrics of this type of process see, for example, Ghysels, Harvey & Rena ult (1996). If Y ∈ BSM then its ex-”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, background): “…choice of M in the construction of realized volatility measures have recently been the subject of intensive research efforts; e.g., Nielsen and Frederiksen (2008), Ait-Sahalia et al. (2005), Bandi and Russell (2008), Barndorff-Nielsen et al. (2008), and Hansen and Lunde (2006), among many others.”
HAR Modeling for Realized Volatility Forecasting (2012, neutral): “3 In the presence of microstructure noise, other estimators are preferable such as the two-scale estimator proposed by [74], the realized kernels method of [13], the pre-average approach of [55], or the multi-scales Discrete Sine Transform estimator ( DST ) of [40].”
High-dimensional minimum variance portfolio estimation based on high-frequency data (2020, background): “…(MSRV, Zhang (2006)), preaveraging estimator (PAV, Jacod et al. (2009), Podolskij and Vetter (2009) and Jacod et al. (2019)), realized kernels (RK, Barndorff-Nielsen et al. (2008)), quasi-maximum likelihood estimator (QMLE, Xiu (2010)), estimated-price realized volatility (ERV, Li et al. (2016))…”
Realising the future: forecasting with high frequency based volatility (HEAVY) models (2010, background): “We follow precisely the bandwidth choice of H spelt out in Barndorff-Nielsen et al. (2009a), to which we refer the reader for details.”
Jumps and Betas: A New Framework for Disentangling and Estimating Systematic Risks (2010, neutral): “Ways in which to best deal with the market microstructure ”noise” in the implementation of univariate realized variation measures is currently a very active area of research; see, e.g., Zhang et al. (2005), Hansen and Lunde (2006), Barndorff-Nielsen et al. (2008), and the references therein.”
Bipower-type estimation in a noisy diffusion setting (2008, background): “[5] proposed a kernel based estimator, which essentially consists of a weighted sum of autocovariances.”