A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data

By P. R. Hansen, Asger Lunde. 2005

319 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/32f03e571357c747091488c613ee5d5cfcfdab9c

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In Atmospheric Science

In Computer Vision and Pattern Recognition

In Global and Planetary Change

Citations in the corpus

Realized range-based estimation of integrated variance (2007, unknown): “3 Then, adopting the notation of Hansen & Lunde (2005), we deﬁne RV at sampling 3 Though an irregular partition of the sampling period suﬃces for consistency, it is standard to compute an equidistant time series of intraday returns by various approaches, such as linear interpolation in, e.g.,…”
Data-based ranking of realised volatility estimators (2011, background): “…volatility forecasts, as opposed to estimators, has received a lot of attention in the econometrics literature, see Poon and Granger (2003) and Hansen and Lunde (2005) for two recent and comprehensive studies, and this is the natural starting point for considering the ranking realised…”
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, background): “Hansen and Lunde (2005) discuss general estimates of the type RMt = δ1 ·RMoct + δ2 · (ycot ) 2 , (14) where RMoct is the realised measure for the open-to-close period in day t, y co t is the overnight return at the opening of the market in day t, and δ1 and δ2 are predetermined values.”
Subsampling Realised Kernels (2007, methodology): “Further, we set 0 = 21=(2 ) in order to standardize E 2t = 1: With this on guration the varian e of R t0 2udu is omparable to the empiri al results found in Hansen and Lunde (2005).”
Garch forecasting performance under different distribution assumptions (2006, unknown): “Instead the scaling estimator presented in Hansen and Lunde (2005b) will be used.”
A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures (2007, methodology): “This approach, along with its pros and cons, is discussed further in Hansen and Lunde (2005) , who also propose an improved estimator by optimally weighting, in a minimum mean-square-error sense, the daytime realized volatility and the squared overnight return.”
Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise (2008, methodology): “However, the empirical work of Hansen and Lunde (2006) suggests this independence assumption is not too damaging statistically when we analyse data in thickly traded stocks recorded every minute.”
A blocking and regularization approach to high dimensional realized covariance estimation (2012, unknown): “In line with recent results by Hansen and Lunde (2010) this finding suggests that RnB estimates are less noisy and thus better reveal the underlying high persistence of the process.”
Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations (2011, methodology): “…recent contributions on integrated variance estimation by virtue of noisy high-frequency asset price data rely on a similar asymptotic condition for " near-consistency " or consistency (see, e.g., Barndor¤-Nielsen et al., 2005, 2008, Hansen and Lunde, 2006, Zhang, 2006, and Zhang et al., 2005).”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “This corresponds with the findings of Fleming et al. (2003).”
Modelling and Forecasting Multivariate Realized Volatility (2011, methodology): “the scaling method introduced by Hansen & Lunde (2005) adapted to the multivariate”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “Hansen and Lunde (2005a) and Patton (2005) have focused on the delicate implications of the use of different loss functions to discriminate between competing forecasting models, where the object of the forecasting is V̂i or a function of it.”
Return distributions and volatility forecasting in metal futures markets: Evidence from gold, silver, and copper (2011, unknown): “Second, similar to Hansen and Lunde (2006) we eliminate days when the opening and closing transaction prices are the same.”
Measuring volatility with the realized range (2006, background): “See Hansen and Lunde (2004) for a theoretical justification of this scaling estimator.”
Properties of Range-Based Volatility Estimators (2012, unknown): “In testing for bias in estimators of , they correctly adjust qc 2 s using formula (15), but they do not adjust qc 2 P and qd 2 GK by constants cP and cGK .”