Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

By Torben G. Andersen, Tim Bollerslev, Francis X. Diebold. Review of Economics and Statistics, 2007

1,644 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/88789dacdd0fd7e87fd8982dc9ed5d6d83ba08b1

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Citations in the corpus

Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, positive): “1 Recent reviews include Andersen et al. (2009) and Barndorff-Nielsen and Shephard (2007). theoretical advances continue to be important, part of the research in this new field has focused on the time-series properties and forecast improvements that realized volatility provides. Examples include And”
Forecasting Volatility of the U.S. Oil Market (2014, positive): “According to Andersen et al. (2007) the model has shown remarkably good forecasting performance comparable to the much more complicated long-memory ARFIMA model.”
Realised quantile-based estimation of the integrated variance (2010, methodology): “bi-power variation (BPV) of Barndorff-Nielsen and Shephard (2004), threshold realised variance (TRV) of Jacod (2008), Mancini (2004a, 2006), and MedRV of Andersen et al. (2008):”
Robust Forecasting of Dynamic Conditional Correlation GARCH Models (2013, methodology): “The most widely used models for forecasting conditional covariances and correlations 1See e.g. Andersen, Bollerslev, and Diebold (2007), Bauwens and Storti (2009) and Carnero, Peña, and Ruiz (2012).”
Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, methodology): “Compared to Andersen et al. (2007b) we accommodate the issue of microstructure noise more directly through the generalized signature plots and rely on the new sequential jump detection technique.”
Expected Stock Returns and Variance Risk Premia (2009, positive): “Numerous parametric and nonparametric volatility forecasting procedures have been proposed in the literature (see, for example, Andersen et al. 2006). We here rely on the simple-to-implement, yet empirically highly accurate, reduced form HAR-RV model advocated by Corsi (2004) and Andersen, Bollersle”
The Volatility of Realized Volatility (2005, methodology): “It is therefore common practice to handle this trade-off by summing returns over 5 or 30 minutes (see, for example, Andersen et al., 2007, 2001b; Barndorff-Nielsen and Shephard, 2004b).2 Given the high liquidity of the S&P 500 index futures market, we follow Andersen and Bollerslev (1998), Andersen…”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “…in forecasting volatility and shows its outstanding performance in capturing "stylized facts" in financial market, such as long memory and multi-scale behavior of volatility (Andersen et al., 2007; Corsi et al., 2010; Bekaert and Hoerova, 2014; Duong and Swanson, 2015; Bollerslev et al., 2016).”
Forecasting global stock market implied volatility indices (2018, background): “…autoregressive models (HAR) are more successful in forecasting volatility due to the fact that they are parsimonious and they can capture the longmemory that is observed in volatility (see, inter alia, Andersen et al., 2007; Corsi, 2009; Busch et al., 2011; Fernandes et al., 2014, Sevi, 2014).”
Realizing smiles: Options pricing with realized volatility (2013, neutral): “Andersen, Bollerslev, Diebold and Labys (2000), (2001), (2003) , Andersen, Bollerslev, Diebold and Ebens (2001), and Andersen et al. (2007), indeed showed that, when daily returns are standardized by the corresponding daily RV, the resulting distribution is nearly Gaussian.”
Market Reaction to Corporate Press Releases (2013, background): “In particular, recent research in financial econometrics is often concerned with the role of jumps in the return generating process (see, e.g., Andersen, Bollerslev, and Diebold (2007)).”
Volatility in Equilibrium: Asymmetries and Dynamic Dependencies (2009, positive): “…too frequently and the loss of important information concerning fundamental price movements when sampling more coarsely; see, e.g., the discussion and references in Andersen et al. (2007b), where the same futures data and five-minute sampling frequency have been used from a different perspective.”
The VIX, the Variance Premium and Stock Market Volatility (2013, neutral): “…in the most recent return variances (Corsi, 2009), asymmetry between positive and negative return shocks (the classic volatility asymmetry, see e.g. Engle and Ng, 1993) and potentially differing predictive information present in jump versus continuous volatility components (Andersen et al., 2007).”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, neutral): “The inclusion of these estimators is motivated by studies such as Andersen et al. (2007) and Patton and Sheppard (2011), which report that the predictability of the integrated variance component of quadratic variation is stronger than the jump component, and thus there may be gains to separately…”
Realized Volatility: A Review (2008, background): “2Other empirical quantities include the bi-power variation and multipower variation that are particularly useful for detecting jumps (see Andersen et al., 2007; Barndorff-Nielsen and Shephard, 2004a, 2005a,b; Barndorff-Nielsen et al., 2006c; Bollerslev et al., 2005; Huang and Tauchen, 2005; Tauchen…”