Academic Engagement (Representative) |
Publications |
[1] Xiafei Li, Chao Liang, Feng Ma. Forecasting Stock Market Volatility with A Large Number of Predictors: New Evidence from the MS-MIDAS-LASSO Model. Annals of Operations Research, 2022, On Line. [2] Xiafei Li, Chao Liang, Zhonglu Chen, et al. Forecasting Crude Oil Volatility with Uncertainty Indicators: New Evidence. Energy Economics, 2022, 108: 105936. [3] Xiafei Li, Chao Liang, Feng Ma. Financial Stress Spillover Network Across Asian Countries in The Context of COVID-19. Applied Economics Letters, 2022, On Line. [4] , , . Can the Return Connectedness Indices from Grey Energy to Natural Gas Help to Forecast the Natural Gas Returns? Energy Economics, 2022, 109: 105947. [5] , , , . Forecasting China’s Stock Market Volatility with Shrinkage Method: Can Adaptive Lasso Select Stronger Predictors from Numerous Predictors? International Journal of Finance & Economics, 2022, On Line. [6] Yu Wei, Lan Bai, . Normal and Extreme Interactions Among Nonferrous Metal Futures: A New Quantile-frequency Connectedness Approach. Finance Research Letters, 2022, 47（Part B）: 102855. [7] , , . Forecasting China’s Crude Oil Futures Volatility: New Evidence from the MIDAS-RV Model and COVID-19 Pandemic. Resources Policy, 2022, 75: 102453. [8] Xiaofei Li, Bo Li, Guiwu Wei, et al. Return Connectedness Among Commodity and Financial Assets During the COVID-19 Pandemic: Evidence from China and The US. Resources Policy, 2021, 73: 102166. [9] , , , et al. Forecasting Regular and Extreme Gold Price Volatility: The Roles of Asymmetry, Extreme Event and Jump. Journal of Forecasting, 2021, 40(08): 1501-1523. [10] , , Xiaodan Chen, et al. Which Uncertainty Is Powerful to Forecast Crude Oil Market Volatility? New Evidence. International Journal of Finance & Economics, 2020, On Line. [11] , . The Dependence and Risk Spillover Between Crude Oil Market and China Stock Market: New Evidence from A Variational Mode Decomposition-Based Copula Method. Energy Economics, 2018, 74: 565-581. |
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