Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models

By Paolo Agnolucci. 2009

316 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/950827ba758afe0a0080014e03c374c64b016c62

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. George Filis
2. Stavros Degiannakis *
3. Fotios Petropoulos
4. Konstantinos Nikolopoulos
5. Duc Khuong Nguyen
6. Yue‐Jun Zhang
7. Rob J Hyndman
8. Rangan Gupta

In Finance

1. George Filis
2. Stavros Degiannakis *
3. Duc Khuong Nguyen
4. Mohamed Arouri
5. Sang Hoon Kang
6. Amine Lahiani
7. Rangan Gupta
8. Seong‐Min Yoon

Citations in the corpus

• Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models (2012, methodology): “As far as we know, two studies are concerned by the modeling and forecasting of the volatility in crude-oil futures using GARCH-family models (Sadorsky, 2006; Agnolucci, 2009).”
• Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014, unknown): “…with previous studies, our dataset is extended to cover the spot and futures prices of natural gas, gold and silver, in addition to the frequently studied spot and futures price of crude oil (e.g., Agnolucci, 2009; Arouri et al., 2012b; Kang et al., 2009; Sadorsky, 2006; Wei et al., 2010).”
• Modeling energy price dynamics: GARCH versus stochastic volatility (2016, background): “, 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) and Manera, Nicolini, and Vignati (2014).”
• Interpreting the crude oil price movements: Evidence from the Markov regime switching model (2015, background): “Typical literature Models Main results Narayan and Narayan [6] The EGARCH model The external shocks have permanent and asymmetric effects on volatility and the behavior of crude oil prices tends to change over short time periods during 1991–2006 Agnolucci [7] The GARCH models and the implied volatil”
• How Does Oil Market Uncertainty Interact with Other Markets? An Empirical Analysis of Implied Volatility Index (2013, unknown): “[36]).”
• Long memory and structural breaks in modeling the return and volatility dynamics of precious metals (2012, background): “…prices as well as nonlinearities, asymmetries and other distributional characteristics such as time-varying conditional moments, volatility clustering and long-persistence of commodity price returns (Sa-3 dorsky, 2006; Agnolucci, 2009; Akram, 2009; Lescaroux, 2009; Browne and Cronin, 2010).”
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “Modeling and predicting oil price volatility are investigated based on the framework of the GARCH-class models (e.g., Agnolucci, 2009; Cheong, 2009; Kang et al., 2009; Mohammadi and Su, 2010; Wei et al., 2010; Nomikos and Pouliasis, 2011; Nomikos and Andriosopoulos, 2012; Wang and Wu, 2012; Efimova…”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “5 Similarly, several other authors model the conditional volatility of oil prices and forecast these volatilities, using univariate models such as the FIAPARCH, HYGARCH, EGARCH, FIGARCH, APARCH, as well as multivariate models such as the BEKK, VAR and Risk Metrics (see, Agnolucci, 2009; Wei et al., ”