Realized volatility forecasting and market microstructure noise

By Torben G. Andersen, Tim Bollerslev, Nour Meddahi. 2011

244 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/34ae6555bc2cd485ed9e732452c7262a25719e7f

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Citations in the corpus

The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, methodology): “Andersen, Bollerslev, and Meddahi (2011a), Ghysels and Sinko (2011) and Asai, McAleer, and Medeiros (2012) consider the impact of measurement noise in forecasting realised volatility.”
Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “Recent studies are Engle et al. (2013), Asgharian et al. (2013) and Conrad and Loch (2015).”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “Forecasting in the presence of microstructure ‘‘noise’’ has also been studied by Aït-Sahalia and Mancini (2008), Andersen et al. (2011), Ghysels and Sinko (2011) and Bandi et al. (2013).”
ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR (2013, background): “See also Meddahi (2001), Meddahi (2002), Andersen et al. (2004), and Andersen, Bollerslev, and Meddahi (2011).”
Integrated volatility and round-off error (2009, methodology): “In this setting, a common convergent estimator of the integrated volatility, with rate n−1/2 and feasible asymptotic theory, is given by the realized volatility, that is, for the absolute integrated volatility n∑ i=1 (Xi/n −X(i−1)/n)2 and for the relative integrated volatility n∑ i=1 (log(Xi/n)− log”
Multivariate High-Frequency-Based Volatility (HEAVY) Models (2012, methodology): “Forecasting the realized measure itself has been the focus of a number of recent studies, e.g. Andersen et al. (2003, 2007, 2011).”
Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “Many studies have shown that these HAR type models offer better forecasting ability than GARCH, SV, VAR-RV and ARFIMA-RV models (see for example Andersen et al 2011; Çelik and Ergin 2014; Sévi 2014) indicating the importance of intraday volatility in forecasting future volatility.”
Can Internet search queries help to predict stock market volatility (2016, unknown): “We compute these price changes over 10 minute intervals in order to circumvent the well-documented microstructure eﬀects (see e.g. Andersen et al. 2003, Andersen, Bollerslev and Meddahi 2011, Ghysels and Sinko 2011).”
Data-based ranking of realised volatility estimators (2011, methodology): “variation of the IBM price process, relative to the corresponding distance using 5-minute calendartime RV on trade prices10, using the QLIKE distance measure presented in equations (4).”
Noisy Prices and Inference Regarding Returns (2013, unknown): “1See, for example, Aı̈t-Sahalia, Mykland, Zhang (2005), Bandi and Russell (2006), Engle and Sun (2007), and Andersen, Bollerslev, and Meddahi (2011).”
Does Realized Skewness Predict the Cross-Section of Equity Returns? (2015, background): “…true price pt: Several studies use Monte Carlo simulations to investigate the properties of realized variance estimators when allowing for market microstructure noise, see for instance Andersen, Bollerslev, and Meddahi (2011), Gonçalves and Meddahi (2009), and Ait-Sahalia and Yu (2009).”