Forecasting crude oil market volatility: Further evidence using GARCH-class models

By Yu Wei, Yudong Wang, Dengshi Huang. 2010

383 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b8b909f01f1729a31c6601fa6a7f780020dd7b73

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Citations in the corpus

Interpreting the crude oil price movements: Evidence from the Markov regime switching model (2015, methodology): “[8] Several linear models and GARCH-class models The GARCH models exhibit greater forecasting accuracy than the linear ones in capturing long-memory and/or asymmetric volatility Hou and Saurdi [9] A GARCH model involving nonparametric method and common GARCH models The nonparametric GARCH model outp”
Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014, background): “…have extensively investigated the issue of volatility modeling and forecasting for crude oil and related petroleum products (Arouri et al., 2012b; Wei et al., 2010; and references therein), none of them have extended their samples to include natural gas, gold, and silver at the same time in…”
Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “…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 and Serletis, 2014).”
Modeling energy price dynamics: GARCH versus stochastic volatility (2016, background): “These models are often compared by evaluating their volatility forecasts (e.g., Sadorsky, 2006; Wei et al., 2010) or the Values-at-Risk (e.”