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

By Ole E. Barndorff-Nielsen, N. Shephard. 2000

399 citations recorded by the source corpus.

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Citations in the corpus

Bipower-type estimation in a noisy diffusion setting (2008, unknown): “See for example Andersen et al. [3] or Barndorff-Nielsen and Shephard [6].”
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, unknown): “As an alternative measure to realized volatility, Barndor¤-Nielsen and Shephard (2002) and Barndor¤-Nielsen and Shephard (2003) have proposed realized power variation - the sum of intraday absolute returns - when there are jumps in the price process.”
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise (2004, background): “The key developments have been the use of estimators of quadratic variation, (e.g. Andersen, Bollerslev, Diebold & Labys (2003) and Barndorff-Nielsen & Shephard (2002))and making sense of their properties when applied to 5 to 30 minute return data.”
A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “Hence, our model is a multivariate extension of the univariate non-Gaussian OU type stochastic volatility model proposed by Barndorff-Nielsen and Shephard (2001, 2002) and used and studied heavily since then (see e.g. Griffin and Steel, 2006, Rheinländer and Steiger, 2006, Roberts,…”
Continuous time volatility modelling: COGARCH versus Ornstein-Uhlenbeck models (2006, methodology): “We have compared the probabilistic properties of both the stochastic volatility model of Barndorff-Nielsen and Shephard and the COGARCH process.”
Power variation and stochastic volatility: a review and some new results (2004, background): “On account of the local Riemann integrability of H, the equality (5) is satisfied for all positive y if and only if it holds for one such y. In Barndorff-Nielsen and Shephard (2003c) the following result was proved (recall the notation (4)), extending an earlier result of Barndorff-Nielsen and Sheph”
Measuring Downside Risk - Realised Semivariance (2008, unknown): “Then, writing jumps in Y as ∆Yt = Yt − Yt−, then [Y ]t = ∫ t 0 σ2sds+ ∑ s≤t (∆Ys) 2 , 1When there are market frictions it is possible to correct this statistic for their effect using the two scale estimator of Zhang, Mykland, and Aı̈t-Sahalia (2005), the realised kernel of Barndorff-Nielsen, Hansen,”
Integrated OU Processes and Non‐Gaussian OU‐based Stochastic Volatility Models (2003, methodology): “Andersen et al. (2000, 2001a) have recently used realized variance estimators of sn to claim that actual variance is typically close to being lognormal for a wide range of h (see also Barndorff-Nielsen & Shephard, 2002).”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “O t h e r M e t h o d s o f V o l a t i l i t y M o d e l i n g “Stochastic volatility” models (Barndorff-Nielsen et al. (2001), Chib et al. (1998), Ghysels et al. (1996), Harvey and Shephard (1993), Jacquier et al. (1994), Shephard (1996), Taylor (1994)), “implied volatility” models (Day and Lewis ”
Extremal behavior of stochastic volatility models (2006, unknown): “∗Center for Mathematical Sciences, Munich University of Technology, D-85747 Garching, Ger- many, email: {fasen, cklu, lindner}@ma.tum.de, www.ma.tum.de/stat/”
Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps (2007, unknown): “More recently, the concept of realised bipower variation has built a nonparametric framework for backing out several variational measures of volatility (see, e.g., Barndorff-Nielsen and Shephard [8] or Barndorff-Nielsen, Graversen, Jacod, Podolskij and Shephard [5]), which has led to a new developme”
Rolling-Sample Volatility Estimators (2002, methodology): “Barndorff-Nielsen and Shephard (2000) introduce a class of non-Gaussian Ornstein–Uhlenbeck Lévy-type processes and characterize analytically the quadratic variation.”
Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading (2011, unknown): “Throughout our analysis we will estimate quantities each day, in the tradition of the realised volatility literature following, for example, Andersen et al. (2001) and Barndorff-Nielsen & Shephard (2002).”