Modeling Jump and Continuous Components in the Volatility of Oil Futures
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Tseng-Chan Tseng
In this study, we use the 'heterogeneous autoregressive' (HAR) model and replace all squared returns with a squared range to estimate realized range-based volatility (RRV) forecasts for oil futures prices. Our findings demonstrate that the HAR-RRV models, involving volatility measures with a realized range-based estimator, successfully capture the long-term memory behavior of volatility in oil futures contracts. We find that realized range-based bi-power variation (RBV), which is also immune to jumps, is a better regressor for future volatility prediction, significantly outperforming the AR model. Similar to the findings for financial markets, we also find that the jump components of RRV have little predictive power for oil futures contracts.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Asymmetry in Stochastic Volatility Models: Threshold or Correlation?
- Inspecting the Poverty-Trap Mechanism: A Quantile Regression Approach
- Mixed Exponential Power Asymmetric Conditional Heteroskedasticity
- Multivariate Extension of the Hodrick-Prescott Filter-Optimality and Characterization
- Modeling Jump and Continuous Components in the Volatility of Oil Futures
Artikel in diesem Heft
- Article
- Asymmetry in Stochastic Volatility Models: Threshold or Correlation?
- Inspecting the Poverty-Trap Mechanism: A Quantile Regression Approach
- Mixed Exponential Power Asymmetric Conditional Heteroskedasticity
- Multivariate Extension of the Hodrick-Prescott Filter-Optimality and Characterization
- Modeling Jump and Continuous Components in the Volatility of Oil Futures
