News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons

By Xilong Chen, Eric Ghysels. 2011

273 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a247df97322f51b7c32a6e63e0cdf415c6b5321f

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. Jennie Bai
2. Eric Ghysels
3. Jonathan H. Wright
4. Lili Ding
5. Wang Kang
6. Xin Zhao
7. Meng Han
8. Pilar Abad

In Finance

1. Jennie Bai
2. Eric Ghysels
3. Jonathan H. Wright
4. Lili Ding
5. Wang Kang
6. Xin Zhao
7. Meng Han
8. Pilar Abad

Citations in the corpus

• Can Internet search queries help to predict stock market volatility (2016, unknown): “The HAR model has been found to capture the long-memory properties of realized volatility very well and has recently been used for example by Andersen, Bollerslev and Diebold (2007), Chen and Ghysels (2011) and Chiriac and Voev (2011).”
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, unknown): “…which include: HAR-RV (Corsi, 2009), HAR-RV-J and HAR-RV-CJ (Andersen et al., 2007a, 2007b), HAR-RV-TJ (Corsi et al., 2010), HAR-S-RV-J (Chen and Ghysels, 2011), HAR-RV-PS2 and HAR-RV-PS3 (Patton and Sheppard, 2015), HAR-CSJ and HAR-CSJd (Sévi, 2014), HAR-ARJ (Prokopczuk et al., 2016),…”
• State Space Models and MIDAS Regressions (2013, methodology): “In the case of mixed sampling frequencies this steady state Kalman filter has a periodic structure and this maps exactly into a multiplicative MIDAS regression model considered by Chen and Ghysels (2009) and Andreou, Ghysels, and Kourtellos (2008b).”
• A comprehensive review of Value at Risk methodologies (2014, methodology): “Recently, Chen et al. (2011) proposed a non-linear dynamic quantile family as a natural extension of the AS model. Although empirical literature on CAViaR method is not extensive, the results seem to indicate that the CAViaR model proposed by Engle and Manganelli (2004) fails to provide accurate VaR”
• Macroeconomics and the Reality of Mixed Frequency Data (2016, methodology): “In addition, the mixed frequency VAR model is a multivariate extension of MIDAS regressions proposed in recent work by Ghysels et al. (2006), Ghysels andWright (2009), Andreou et al. (2010) and Chen and Ghysels (2011), among others.”
• Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “For the HAR-RSV model of Patton and Sheppard (2011) and the extension HAR-RSV-J model of Chen and Ghysels (2011), we find that the jump component is statistically significant as are the RV components decomposed into positive and negative realized semivariances.”
• The VIX, the Variance Premium and Stock Market Volatility (2013, unknown): “It is now generally accepted thatmodels based on high frequency realized variances dominate standardmodels in theGARCH class (see e.g. Chen and Ghysels, 2012) and we therefore examine the state-of-the-art models in that class.”