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Do extreme shocks help forecast oil price volatility? : the augmented GARCH-MIDAS approach

Wang, Lu; Ma, Feng; Liu, Guoshan; Lang, Qiaoqi - In: International journal of finance & economics : IJFE 28 (2023) 2, pp. 2056-2073

Persistent link: https://www.econbiz.de/10014253654

Oil futures volatility predictability : evidence based on Twitter-based uncertainty

Lang, Qiaoqi; Lu, Xinjie; Ma, Feng; Huang, Dengshi - In: Finance research letters 47 (2022) 1, pp. 1

Persistent link: https://www.econbiz.de/10013457290

Is Baidu index really powerful to predict the Chinese stock market volatility? : new evidence from the internet information

Lang, Qiaoqi; Wang, Jiqian; Ma, Feng; Huang, Dengshi; … - In: China finance review international 13 (2023) 2, pp. 263-284

Persistent link: https://www.econbiz.de/10014312401

Forecasting stock price volatility : new evidence from the GARCH-MIDAS model

Wang, Lu; Ma, Feng; Liu, Jing; Yang, Lin - In: International journal of forecasting 36 (2020) 2, pp. 684-694

Persistent link: https://www.econbiz.de/10012415334

Forecasting crude oil volatility with geopolitical risk : do time-varying switching probabilities play a role?

Wang, Lu; Ma, Feng; Hao, Jianyang; Gao, Xinxin - In: International review of financial analysis 76 (2021), pp. 1-12

Persistent link: https://www.econbiz.de/10012804675

Prediction of Crude Oil Prices in a Mixed-Frequency Data-Rich Condition : New Evidence from the LASSO-MIDAS Model

Hao, Jianyang; wang, lu; Wahab, M.I.M; Ma, Feng - 2022

In a data-rich environment, few studies use many predictors at different frequencies to forecast crude oil returns. This paper proposes a new model, LASSO-MIDAS, which combines LASSO (the least absolute shrinkage and selection operator) and MIDAS (mixed data sampling model) to predict crude oil...

Persistent link: https://www.econbiz.de/10013308801