Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements

By S. J. Koopman, Borus Jungbacker, E. Hol. 2005

611 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/11272b5b089cc29f81f121e16805a30777d7968f

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Citations in the corpus

The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data (2014, background): “Koopman et al. (2005) propose that both implied volatility and realized volatility are informationally accurate.”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, neutral): “Martens (2002) documents that the overnight volatility represents an important fraction of total daily volatility, hence incorporating the cross-product of overnight returns as in (1) is important for accurately measuring (co-)variances, see also Fleming et al. (2003) and Hansen and Lunde (2005) for”
Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “Although the ARFIMA model achieves a higher forecast accuracy than GARCH-class models (e.g., Koopman et al., 2005; Liu and Wan, 2012), Corsi (2009) points out that the ARFIMA model is just a convenient mathematical trick, lacks a clear economic interpretation, and leads to the loss of information…”
A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data (2005, neutral): “3 This estimator has previously been applied by Hansen & Lunde (2005b), and a similar estimator, δ̃ ≡ ( ∑n t=1 r 2 2,t + ∑n t=1 r 2 1,t)/ ∑n t=1 r 2 2,t , was concurrently suggested by Martens (2002) and Koopman et al. (2005). The latter is expected to be less precise than δ̂, because var(r2 2,t) is”
ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR (2013, neutral): “These methods have been applied to realized measures of volatility by BarndorffNielsen and Shephard (2002), Barndorff-Nielsen, Nielsen, Shephard, and Ysusi (2004), Hansen and Lunde (2005b), and Koopman, Jungbacker, and Hol (2005).”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “Overall, HAR-type models perform better than GARCH-class models in capturing the volatility dynamics and are more widely used in highfrequency data (see, e.g., Andersen et al., 2003; Hansen and Lunde, 2005; Koopman et al., 2005; Wei et al., 2010; Çelik and Ergin, 2014; Ma et al., 2015).”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, neutral): “…forecasting models in which the realized volatilities are modeled by standard discrete-time time series procedures, examples of which include Andersen et al. (2003) (2007), Corsi (2004), Corsi et al. (2007), Deo et al. (2006), Koopman et al. (2005) and Martens et al. (2004), among others.”
Return distributions and volatility forecasting in metal futures markets: Evidence from gold, silver, and copper (2011, neutral): “The criteria for evaluating forecast performance are dependent on the relative values of the loss functions (see Andersen et al. 2001; Baillie & Bollerslev, 1992; Koopman et al., 2005; Reno & Barucci, 2002).”
Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, neutral): “…(2003), Andersen, Bollerslev, and Diebold (2007), Andersen, Bollerslev, and Meddahi (2004), Ghysels and Sinko (2006), Ghysels, Santa-Clara, and Valkanov (2006), Koopman, Jungbacker, and Hol (2005), Maheu and McCurdy (2002, 2007), Martens, van Dijk, and de Pooter (2003), and Taylor and Xu (1997).”
Realized Volatility: A Review (2008, background): “Deo et al. (2006) considered a long-memory stochastic volatility model and Koopman et al. (2005) proposed a model combining unobserved components and longmemory.”
Realized volatility forecasting and market microstructure noise (2011, methodology): “The model specific nature of these earlier studies was largely circumvented by the subsequent approach of Andersen, Bollerslev and Meddahi (henceforth ABM)(2004, 2005) who utilize the so-called eigenfunction stochastic volatility (ESV) framework of Meddahi (2001) in the development of analytic expre”