A Nonparametric GARCH Model of Crude Oil Price Return Volatility
By A. Hou, Sandy Suardi. 2012
187 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/39374a2f857349f431c5676a8560ec75fba79464
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In Economics and Econometrics
- Stavros Degiannakis *
- George Filis
- Yue‐Jun Zhang
- Ruijin Du
- Minggang Wang
- R. Cong
- H. Eugene Stanley
- Lin Chen
In Finance
- George Filis
- Stavros Degiannakis *
- Lu Zhang
- Yue‐Jun Zhang
- R. Cong
- H. Eugene Stanley
- Alex Y. Lo
- R. Brooks
Citations in the corpus
- A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “The second is a time series approach, including ARIMA [10], GARCH-type models [11 – 15] et al., only looking at the history of price to determine future price movement.”
- Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “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., 20”
- Emission trading and carbon market performance in Shenzhen, China (2017, background): “There are also many studies on the relationship between risk and return of bulk commodities, such as oil prices [75] and agricultural products prices [76, 77].”
- 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”
- Forecasting crude oil price volatility (2018, unknown): “Furthermore, these models have been shown to have good out-of-sample performance when forecasting oil price volatility at short horizons (Mohammadi and Su 2010, and Hou and Suardi 2012).”