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Do oil-price shocks predict the realized variance of U.S. REITs?
Bonato, Matteo; Çepni, Oğuzhan; Gupta, Rangan; … - In: Energy economics 104 (2021), pp. 1-19
Persistent link: https://www.econbiz.de/10013364407
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Forecasting the realized variance of oil-price returns using machine learning : is there a role for U.S. state-level uncertainty?
Çepni, Oğuzhan; Gupta, Rangan; Pienaar, Daniel; … - In: Energy economics 114 (2022), pp. 1-14
Persistent link: https://www.econbiz.de/10013477404
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Volatility spillovers in commodity markets : a large t-vector autoregressive approach
Barbaglia, Luca; Croux, Christophe; Wilms, Ines - In: Energy economics 85 (2020), pp. 1-11
Persistent link: https://www.econbiz.de/10012509561
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Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models
Qu, Hui; Chen, Wei; Niu, Mengyi; Li, Xindan - In: Energy economics 54 (2016), pp. 68-76
Persistent link: https://www.econbiz.de/10011662756
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Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets
Qu, Hui; Duan, Qingling; Niu, Mengyi - In: Energy economics 74 (2018), pp. 767-776
Persistent link: https://www.econbiz.de/10011972967
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Multi-perspective investor attention and oil futures volatility forecasting
Qu, Hui; Li, Guo - In: Energy economics 119 (2023), pp. 1-13
Persistent link: https://www.econbiz.de/10014279697
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Forecasting excess stock returns with crude oil market data
Liu, Li; Ma, Feng; Wang, Yudong - In: Energy economics 48 (2015), pp. 316-324
Persistent link: https://www.econbiz.de/10011533825
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The role of news sentiment in oil futures returns and volatility forecasting : data-decomposition based deep learning approach
Li, Yuze; Jiang, Shangrong; Li, Xuerong; Wang, Shouyang - In: Energy economics 95 (2021), pp. 1-11
Persistent link: https://www.econbiz.de/10012816879
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Forecasting crude oil and refined products volatilities and correlations : new evidence from fractionally integrated multivariate GARCH models
Marchese, Malvina; Kyriakou, Ioannis; Tamvakis, Michael; … - In: Energy economics 88 (2020), pp. 1-13
Persistent link: https://www.econbiz.de/10012516745
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Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? : new evidence
Wang, Jiqian; Huang, Yisu; Ma, Feng; Chevallier, Julien - In: Energy economics 91 (2020), pp. 1-13
Persistent link: https://www.econbiz.de/10012518664