Forecasting crude oil market volatility: A Markov switching multifractal volatility approach

By Yudong Wang, Chongfeng Wu, Li Yang. 2016

110 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e8d7b2e7bd173b13dec2275790c51f6553381824

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. Jinliang Zhang
2. Yue‐Jun Zhang
3. Ling‐Yun He
4. R. Ripple
5. Ting Yao
6. Muhammad Shahbaz
7. Zeeshan Khan
8. Ilhan Ozturk

In Finance

1. Yue‐Jun Zhang
2. Ting Yao
3. Jinliang Zhang
4. Ling‐Yun He
5. R. Ripple

Citations in the corpus

• Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, background): “The descriptive statistics of WTI and Brent returns are similar, and it can be explained by the highly integrated world crude oil markets (Wang et al., 2016).”
• Volatility forecasting of crude oil market: A new hybrid method (2018, methodology): “For the application of models to predict commodity or financial market price volatility states, the Markov regime switching (MRS) model has been universally recognized in existing studies (Balcilar et al., 2015; Wang et al., 2016; Zhang & Zhang, 2015).”