Realized volatility in the futures markets

By Dimitrios D. Thomakos, Tao Wang. 2003

139 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/42f77826e70a8ac6bad1847c4cb3ee247f43d5e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Stavros Degiannakis *
2. George Filis
3. Tim Bollerslev
4. Fulvio Corsi
5. Torben G. Andersen
6. Andrew J. Patton
7. Nour Meddahi
8. Christian Pigorsch

In Finance

1. George Filis
2. Stavros Degiannakis *
3. Tim Bollerslev
4. Fulvio Corsi
5. Andrew J. Patton
6. Torben G. Andersen
7. Nour Meddahi
8. Kevin Sheppard

Citations in the corpus

• Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility (2003, unknown): “…also motivated the estimation of long-memory type ARFIMA models for realized volatility in ABDL (2003), Areal and Taylor (2002), Deo, Hurvich and Lu (2003), Hol and Koopman (2002), Martens, van Dijk, and Pooter (2003), Pong, Shackleton, Taylor and Xu (2003), Thomakos and Wang (2003), among others.”
• Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “…of long-memory type ARFIMA models for realized volatilities in ABDL (2003), Areal and Taylor (2002), Deo, Hurvich, and Lu (2006), Koopman, Jungbacker, and Hol (2005), Martens, van Dijk, and de Pooter (2004), Pong, Shackleton, Taylor, and Xu (2004), and Thomakos and Wang (2003), among others.”
• The Volatility of Realized Volatility (2005, background): “Pong, S., Shackleton, M. B., Taylor, S. J., Xu, X. (2004).”
• A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “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…”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, background): “For instance, authors such as Andersen et al. (2001), Thomakos and Wang (2003) or Wu (2011), opine that night-time periods and weekends could be ignored from the construction of the realized volatility.”
• Realized volatility forecasting and market microstructure noise (2011, background): “…and Taylor (2002), Corsi (2009), Deo, Hurvich and Lu (2006), Koopman, Jungbacker and Hol (2005), Martens (2002), Pong, Shackleton, Taylor and Xu (2004), and Thomakos and Wang (2003), among many others, involving estimation of reduced form forecasting models for various realized volatility series.”
• Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “First, we explore the usefulness of the two time-scales estimator proposed by Zhang et al. (2005),1 which combines the realized covariance matrix constructed using subsampling at a certain frequency and the realized covariance matrix constructed using the highest possible sampling frequency.”
• Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, result): “These numbers are broadly consistent with the results of Thomakos and Wang (2003) and Wang et al. (2008).”
• Properties of Range-Based Volatility Estimators (2012, unknown): “\ 2 GKprecise = 0:511 (h l) 2 0:019 (c(h l) 2hl) 0:383c(2) (7)”