Forecasting volatility of crude oil markets
By Sang Hoon Kang, Seong‐Min Yoon. 2009
378 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/e813fd950ae239b4d338465fc0a26457ae8c355c
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
- George Filis
- Stavros Degiannakis *
- Perry Sadorsky
- Vipin Arora
- Duc Khuong Nguyen
- Mohamed Arouri
- Amine Lahiani
- Shawkat Hammoudeh
In Finance
- George Filis
- Stavros Degiannakis *
- Duc Khuong Nguyen
- Mohamed Arouri
- Shawkat Hammoudeh
- Perry Sadorsky
- Amine Lahiani
- Vipin Arora
Citations in the corpus
- Modeling energy price dynamics: GARCH versus stochastic volatility (2016, background): “…under which the conditional variance is a deterministic function of model parameters and past data (see, e.g., Fong and See, 2001; Sadorsky, 2006; Kang, Kang, and Yoon, 2009; Agnolucci, 2009; Mohammadi and Su, 2010; Nomikos and Andriosopoulos, 2012; Mason and Wilmot, 2014; Manera, Nicolini,…”
- Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, background): “(2014), Nomikos and Pouliasis (2011), Kang et al .”
- Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “For example, Kang et al. (2009) use daily oil spot prices in order to forecast the 1-day, 5days and 20-days ahead conditional volatilities by means of CGARCH, FIGRACH and IGARCH models.”
- On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness (2012, methodology): “GARCH-type approach has received a particular interest from almost all previous papers in modeling financial volatility (Agnolucci, 2009; Hassan and Malik, 2007; and Kang et al., 2009).”
- A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Kang et al. [13] then proposed a variety of conditional volatility models, including GARCH, IGARCH, CGARCH, and FIGARCH, to forecast the volatility of crude oil markets, and found that the CGARCH and FIGARCH models can forecast volatility persistence.”