Econometric analysis of realized volatility and its use in estimating stochastic volatility models

By Ole E. Barndorff-Nielsen, N. Shephard. 2002

2,156 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7450173551701832f36df48ffec39e46190c9d23

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In Economics and Econometrics

Citations in the corpus

Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution (2007, neutral): “s=1 �s 1 i r 2 i;t s 0 3 ) 2See, for example, Litterman and Winkelmann (1998).”
Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) (2006, methodology): “…important result from stochastic analysis, which has found numerous applications in statistical inference for diffusion processes (see for instance Barndorff-Nielsen and Shephard (2002)), is that a continuous time diffusion path on an interval of time [0, t] can be used to estimate perfectly the…”
Microstructure Noise in the Continuous Case: The Pre-Averaging Approach ∗ (2008, neutral): “A limit theory was developed in [8,9].”
Estimation of integrated volatility in stochastic volatility models (2005, background): “Hence knowing the regularity of our partition we can calculate the possible range of p, for example in the most irregular spaces case we have to take 1/2 p. Obviously conditions (8) and (9) imply ( 10 ).”
REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY (2012, methodology): “The theoretical foundation of realized variance was developed in Andersen, Bollerslev, Diebold and Labys (2001) and Barndorff-Nielsen and Shephard (2002).”
Extremal behavior of stochastic volatility models (2006, background): “This is exactly what BarndorffNielsen and Shephard [4,5] have suggested, modelling the (right-continuous) volatility process as a Lévy-OU process.”
Long Memory Dynamics for Multivariate Dependence under Heavy Tails (2014, positive): “Classical realised volatility (RV ) and realised correlation (RCorr) measures can be retrieved in the usual way as described by Andersen, Bollerslev, Diebold, and Labys (2001) and Barndorff-Nielsen and Shephard (2002). We compute the realised measures for each day in our sample.”
Improving Portfolio Selection Using Option-Implied Volatility and Skewness (2013, background): “The idea that option prices contain information about future asset returns has been understood ever since the work of Black and Scholes (1972) and Merton (1973).”
NONPARAMETRIC ESTIMATION OF THE DIFFUSION COEFFICIENT OF STOCHASTIC VOLATILITY MODELS (2008, methodology): “Such an estimator would be infeasi ble, but we can substitute the integrated variance with a consistent estimate of it, namely, realized volatility; see Barndorff-Nielsen and Shephard (2002), Meddahi (2002), and Andersen, Bollerslev, Diebold, and Labys (2003), among others.”
Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model (2016, neutral): “With the advent of high-frequency data, one can estimate ex-post daily return variation with the so-called realized variance (or realized volatility) measures; see Andersen and Bollerslev (1998), Andersen et al. (2001), and Barndorff-Nielsen and Shephard (2002).”
Does Realized Skewness Predict the Cross-Section of Equity Returns? (2015, methodology): “The so-called bipower variation estimator of Barndor¤-Nielsen and Shephard (2004) is dened by”
Correlation Risk and Optimal Portfolio Choice (2010, background): “Further important contributions include Harvey, Ruiz, and Shephard (1994), who specify a model with correlation dynamics that are driven by the same factors affecting volatility, and Barndorff-Nielsen and Shephard (2004). A key feature in our model is that correlations can have dynamics that are not”
Exponential GARCH Modeling With Realized Measures of Volatility (2016, neutral): “Realized measures of volatility, such as the popular realized variance, are computed from high-frequency data, see Andersen et al. (2001) and Barndorff-Nielsen and Shephard (2002).”
ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR (2013, methodology): “In the context of time series of volatility, this result was noted in Barndorff-Nielsen and Shephard (2002) and Meddahi (2003); see also Andersen, Bollerslev, and Meddahi (2004).”