Microstructure noise, realized volatility, and optimal sampling

By Jeffrey R. Russell, F. Bandi. 2004

235 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ffcb143b39ea3d6c62532fd51381df8f4027b456

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Citations in the corpus

Are Volatility Estimators Robust with Respect to Modeling Assumptions (2007, unknown): “[1]; Bandi and Russell [5]; Hansen and Lunde [14]).”
Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets (2006, unknown): “frequently and blurring specific price reactions when sampling too infrequently; see e.g., the related discussions in Andersen et al. (2003b), Bandi and Russell (2004) , Hansen and Lunde (2006) and Ait-Sahalia, Mykland and Zhang (2005), among others.”
Limit theorems for multipower variation in the presence of jumps (2006, background): “Papers which directly model the impact of market microstructure noise on these realised quantities include Bandi and Russell (2003), Hansen and Lunde (2003) and Zhang, Mykland, and Aı̈t-Sahalia (2005). Related work in the probability literature on the impact of noise on discretely observed diffusion”
Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility (2010, unknown): “4 Bandi and Russell (2003) suggested that sample horizon range from 5-minute to 30 minute interval is optimal as the minimization of the conditional mean-squared error of the realized volatility estimator.”
Realized Variance and Market Microstructure Noise (2006, methodology): “Most of the existing literature has established results assuming this kind of noise, and in this section we shall draw on several important results from Zhou (1996), Bandi and Russell (2005), and Zhang et al. (2005). Although we have already dismissed this form of noise as an accurate description of”
Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations (2011, methodology): “[5] The optimal nite sample MSE values of b V Bar and b V ZMA are smaller than those of the classical realized variance estimator.”
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise (2004, methodology): “Because theRV is sensitive to market microstructure noise it is recommended to use sparse sampling in ractice, and the optimal sampling frequency is derived in Bandi & Russell (2004) and Zhang et al. (2004).”
Integrated Covariance Estimation using High-frequency Data in the Presence of Noise (2006, unknown): “We benefited from a number of articles that derive an expression for the bias and a bias-correction of realized variance in the univariate case, such as Bandi and Russell (2005a) , Oomen (2005), Zhang, Mykland, and A¨it-Sahalia (2005), Barndorff-Nielsen, Hansen, Lunde, and Shephard (2006), and Hans”
Semi-Parametric Comparison of Stochastic Volatility Models using Realized Measures (2006, unknown): “This is consistent with the model considered by Bandi and Russell (2003, 2006), Aït-Sahalia et al. (2005a), and Zhang et al. (2005).4 Needless to say, when ν = 0 then t+ j/M = 0 (almost surely, a.s.) and therefore Xt+ j/M = Yt+ j/M (a.s.).”
Realized Beta: Persistence and Predictability (2004, background): “…modeling the high-frequency frictions; see, also Andersen, Bollerslev, Diebold and Labys (2000), Andreou and Ghysels (2002), Hansen and Lunde (2003), Bandi and Russell (2003), and Zhang, Mykland and Ait-Sahalia (2003), among others, for further discussion or alternative procedures in this context.”