Microstructure Noise, Realized Variance, and Optimal Sampling

By F. Bandi, Jefirey R. Russell. 2008

562 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9c01f17a58cef8e7b58a5a2132fa16e0617c7f98

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Citations in the corpus

Forecasting the realized range-based volatility using dynamic model averaging approach (2017, background): “Nevertheless, Bandi and Russell (2008) point out that RV could not identify the daily integrated variance of the frictionless equilibrium price in the presence of market microstructure noise.”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, methodology): “Alternatively, others have studied the use of moderate frequency data such as five minute returns to ameliorate the effect of such noise (e.g., Andersen et al. 2000; Bandi and Russell 2008).”
Realising the future: forecasting with high frequency based volatility (HEAVY) models (2010, background): “1 See also the important work by Bandi and Russell (2008) , Bandi and Russell (2006), Hansen and Lunde (2006),”
Data-based ranking of realised volatility estimators (2011, background): “(2005) and Huang and Tauchen (2005), I set 2 to be such that the proportion of the variance of the 5-minute return (5/390 of a trade day) that is attributable to microstructure noise is 20%: 2 2 V [rt] 5 390 + 2 2 = 0:20 (13)”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “, Bandi and Russell (2008). To this end, we consider estimating RQ based on coarser 15-minute returns. The approximation in equation (4) that motivates the HARQ model is, of course, asymptotic (for M →∞). Finally, we also consider the wild bootstrap of Gonçalves and Meddahi (2009). Specifically, re-”
A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures (2007, background): “…best deal with these complications and the practical choice of M have been the subject of intensive recent research efforts; see, e.g., Äıt-Sahalia, Mykland and Zhang (2005), Bandi and Russell (2008), Barndorff-Nielsen, Hansen, Lunde and Shephard (2008), Hansen and Lunde (2006), and Zhang (2006).”
Risk, Jumps, and Diversification (2008, background): “Ways in which to best deal with these complications and the practical choice of M are the subject of intensive ongoing research efforts; see, e.g., Ait-Sahalia et al. (2005), Bandi and Russell (2008), Barndorff-Nielsen et al. (2006), and Hansen and Lunde (2006).”
Optimal combinations of realised volatility estimators (2009, methodology): “Bandi and Russell (2008) also propose a bias-corrected realised variance estimator, which removes the estimated impact of the microstructure noise; we consider the BandiRussell RV estimator both with ( RVBR,bc ) and without( RVBR ) this bias correction.”
News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons (2011, background): “See for example Aı̈t-Sahalia, Mykland, and Zhang (2005), Bandi and Russell (2006), Bandi and Russell (2005), Barndorff-Nielsen, Hansen, Lunde, and Shephard (2009) and Hansen and Lunde (2006). In our empirical work we consider series for which it is reasonable to assume that they are sampled without ”
Separating Microstructure Noise from Volatility (2006, unknown): “The optimal sampling frequency is chosen as the value δ = 1 M∗ with { M∗ = M : 2M(3)α̂ + M(2)β̂ − 2Q̂i = 0 } , (13)”
Realized volatility forecasting and market microstructure noise (2011, methodology): “Important contributions include the first-order autocorrelation adjustment by Zhou (1996), the notion of an optimal sampling frequency by Bandi and Russell (2006, 2008) and Aı̈t-Sahalia, Mykland and Zhang (2005), the average and two-scale estimator of Zhang, Mykland and Aı̈t-Sahalia (2005), the…”