Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility

By Torben G. Andersen, Tim Bollerslev, Francis X. Diebold. 2003

125 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5d1170e22051c09d7cd8c7ff1bb441991348cdfa

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Citations in the corpus

MIDAS Regressions: Further Results and New Directions (2007, methodology): “These MIDAS regressions are inspired by the HAR model of Corsi (2003), which was also used in Andersen et al. (2003).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “The use of forecasting using estimates of the increments of the components of QV was introduced by Andersen, Bollerslev, and Diebold (2003).”
Variance Dynamics: Joint Evidence from Options and High-Frequency Returns (2011, unknown): “The raw variance estimators are used in early works, e.g., Andersen et al. (2001) and Andersen et al. (2003).”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, methodology): “More recently, several studies, including Andersen et al. (2003), Martens et al. (2004), Martens and Zein (2004), Pong et al. (2004), and Thomakos and Wang (2003) among others, have advocated the use of ARFIMA type models, along with approximate long-memory component type structures in Andersen et…”
Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation (2005, unknown): “Following early drafts of the work reported in this paper our central limit theory for the linear jump statistic has been used by Huang and Tauchen (2003) and Andersen, Bollerslev, and Diebold (2003).”
Measuring the impact of jumps in multivariate price processes using bipower covariation (2004, unknown): “Following the introduction of bipower variation, Andersen, Bollerslev, and Diebold (2003) have used daily increments to realised BPV as an input into new reduced form forecasting devices for modelling future values of daily realised variances (which in turn proxies the variability of future prices).”
VAST VOLATILITY MATRIX ESTIMATION FOR HIGH-FREQUENCY FINANCIAL DATA (2010, background): “For a single asset, estimators of integrated volatility include realized volatility (RV) [Andersen et al. (2003), Barndorff-Nielsen and Shephard (2002)], bi-power realized variation (BPRV) [Barndorff-Nielsen and Shephard (2006)], two-time scale realized volatility (TSRV) [Zhang, Mykland and Aı̈t-Sah”