Academic Engagement (Representative) |
Publications |
[1] Abderrazak Dhaoui, Julien Chevallier, Feng Ma. Identifying Asymmetric Responses of Sectoral Equities to Oil Price Shocks in A NARDL Model. Studies in Nonlinear Dynamics&Econometrics, 2021, 25(2). [2] Botao Lu, Feng Ma, Jiqian Wang, et al. Harnessing the Decomposed Realized Measures for Volatility Forecasting: Evidence from the US Stock Market. International Review of Economics&Finance, 2021, 72(3): 672-689. [3] Chao Liang, Feng Ma, Lu Wang, et al. The Information Content of Uncertainty Indices for Natural Gas Futures Volatility Forecasting. Journal of Forecasting, 2021, 40(7): 1310-1324. [4] Chao Liang, Yan Li, Feng Ma, et al. Global Equity Market Volatilities Forecasting: A Comparison of Leverage Effects, Jumps, and Overnight Information. International Review of Financial Analysis, 2021, 75(8): 101750. [5] Chao Liang, Yu Wei, Likun Lei, Feng Ma. Global Equity Market Volatility Forecasting: New Evidence. International Journal of Finance&Economics, 2021, 27(1): 594-609. [6] Feng He, Feng Ma, Ziwei Wang, et al. Asymmetric Volatility Spillover Between Oil-Importing and Oil-Exporting Countries' Economic Policy Uncertainty and China's Energy Sector. International Review of Financial Analysis, 2021, 75: 101739. [7] Feng Ma, Chao Liang, Qing Zeng, Haibo Li. Jumps and Oil Futures Volatility Forecasting: A New Insight. Quantitative Finance, 2021, 21(5): 853-863. [8] Jiqian Wang, Feng Ma, M.I.M. Wahab, Dengshi Huang. Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect. Journal of Forecasting, 2021, 40(5): 921-941. [9] Jiqian Wang, Feng Ma, Chao Liang, et al. Volatility Forecasting Revisited Using Markov‐switching with Time‐varying Probability Transition. International Journal of Finance&Economics, 2021, 27(1): 1387-1400. [10] Lu Wang, Feng Ma, Jianyang Hao, et al. Forecasting Crude Oil Volatility with Geopolitical Risk: Do Time-varying Switching Probabilities Play a Role? International Review of Financial Analysis, 2021, 76: 101756. [11] Lu Wang, Feng Ma, Tiaojiao Niu, et al. The Importance of Extreme Shock: Examining the Effect of Investor Sentiment on the Crude Oil Futures Market. Energy Economics, 2021, 99: 105319. [12] Xinjie Lu, Feng Ma, Jiqian Wang, Bo Zhu. Oil Shocks and Stock Market Volatility: New Evidence. Energy Economics, 2021, 103: 105567. [13] Yaojie Zhang, Yudong Wang, Feng Ma. Forecasting US Stock Market Volatility: How to Use International Volatility Information. Journal of Forecasting, 2021, 40(5): 733-768. [14] Yaojie Zhang, Feng Ma, Chao Liang, et al. Good Variance, Bad Variance, and Stock Return Predictability. International Journal of Finance&Economics, 2021, 26(3): 4410-4423. [15] Yu Lin, Yan Yan, Jiali Xu, Ying Liao, Feng Ma. Forecasting Stock Index Price Using the CEEMDAN-LSTM Model. The North American Journal of Economics and Finance, 2021, 57: 101421. [16] Wang Ruoxin, Ma Feng. Intraday Return Predictability: Based on Intraday Jumps and Momentum. Systems Engineering Theory and Practice, 2021, 41(08): 2004-2014. [17] Chao Liang, Feng Ma, Ziyang Li, et al. Which Types of Commodity Price Information Are More Useful for Predicting US Stock Market Volatility? Economic Modelling, 2020, 93: 642-650. [18] Chao Liang, Yaojie Zhang, Xiafei Li, Feng Ma. Which Predictor Is More Predictive for Bitcoin Volatility? And Why? International Journal of Finance&Economics, 2020, 27(2): 1947-1961. [19] Dexiang Mei, FengMa, Yin Liao, et al. Geopolitical Risk Uncertainty and Oil Future Volatility: Evidence from MIDAS Models. Energy Economics, 2020, 86: 104624. [20] Feng Ma, Chao Liang, Yuanhui Ma, et al. Cryptocurrency Volatility Forecasting: A Markov Regime‐switching MIDAS Approach. Journal of Forecasting, 2020, 39(8): 1277-1290. [21] Jiqian Wang, Xinjie Lu, Feng He, Feng Ma. Which Popular Predictor Is More Useful to Forecast International Stock Markets During the Coronavirus Pandemic: VIX Vs EPU? International Review of Financial Analysis, 2020, 72: 101596. [22] Jiqian Wang, Yisu Huang, Feng Ma, et al. Does High-frequency Crude Oil Futures Data Contain Useful Information for Predicting Volatility in The US Stock Market? New Evidence. Energy Economics, 2020, 91: 104897. [23] Li Liu, Feng Ma, Qing Zeng, et al. Forecasting the Aggregate Stock Market Volatility in A Data-rich World. Applied Economics, 2020, 52(32): 3448-3463. [24] Lu Wang, Feng Ma, Guoshan Liu. Forecasting Stock Volatility in the Presence of Extreme Shocks: Short‐term and Long‐term Effects. Journal of Forecasting, 2020, 39(5): 797-810. [25] Lu Wang, Feng Ma, Jing Liu, et al. Forecasting Stock Price Volatility: New Evidence from the GARCH-MIDAS Model. International Journal of Forecasting, 2020, 36(2): 684-694. [26] Lu Wang, Feng Ma, Tianjiao Niu, et al. Crude Oil and BRICS Stock Markets Under Extreme Shocks: New Evidence. Economic Modelling, 2020, 86: 54-68. [27] Tao Li, Feng Ma, Xuehua Zhang, et al. Economic Policy Uncertainty and the Chinese Stock Market Volatility: Novel Evidence. Economic Modelling, 2020, 87: 24-33. [28] Wang Chen, Feng Ma, Yu Wei, et al. Forecasting Oil Price Volatility Using High-frequency Data: New Evidence. International Review of Economics&Finance, 2020, 66: 1-12. [29] Xiafei Li, Wei Yu, Xiaodan Chen, Feng Ma. Which Uncertainty Is Powerful to Forecast Crude Oil Market Volatility? New Evidence. International Journal of Finance&Economics, 2020. [30] Yan Li, Chao Liang, Feng Ma, et al. The Role of the IDEMV in Predicting European Stock Market Volatility During the COVID-19 Pandemic. Finance research letters, 2020, 36: 101749. [31] Yan Li, Lian Luo, Chao Liang, Feng Ma. The Role of Model Bias in Predicting Volatility: Evidence from The US Equity Markets. China Finance Review International, 2020. [32] Yaojie Zhang, Feng Ma, Yin Liao. Forecasting Global Equity Market Volatilities. International Journal of Forecasting, 2020, 36(4): 1454-1475. [33] Chen Wang, Ma Feng, Wei Yu, et al. VaR Prediction of China's Stock Market Dynamics from a High-frequency Perspective Model Research. Operations Research and Management, 2020, 29(02): 184-194. [34] Chen Wang, Wei Yu, Ma Feng, et al. A New Method of Stock Market Volatility Forecasting in High-frequency Perspective: HARFIMA Model. Journal of Management Science, 2020, 23(11): 103-116. [35] Wang Lu, Huang Dengshi, Ma Feng, et al. The Impact of Major Emergency on International Foreign Exchange Markets: The Case of Britain Vote to Leave the EU in the Referendum. Mathematical Statistics and Management ,2020, 39(01): 174-190. [36] Feng Ma, M.I.M.Wahab, Yaojie Zhang. Forecasting the US Stock Volatility: An Aligned Jump Index from G7 Stock Markets. Pacific-Basin Finance Journal, 2019, 54: 132-146. [37] Feng Ma, Xinjie Lu, Ke Yang, et al. Volatility Forecasting: Long Memory, Regime Switching and Heteroscedasticity. Applied Economics, 2019, 51(38): 4151-4163. [38] Feng Ma, Yaojie Zhang, M. I. M. Wahab, Xiaodong Lai. The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence[J]. Journal of forecasting, 2019, 38(5): 400-414. [39] Feng Ma, Yin Liao, Yaojie Zhang, et al. Harnessing Jump Component for Crude Oil Volatility Forecasting in the Presence of Extreme Shocks. Journal of Empirical Finance, 2019, 52: 40-55. [40] Jing Hao, Xiong Xiong, Feng He, Feng Ma. Price Discovery in the Chinese Stock Index Futures Market. Emerging Markets Finance and Trade, 2019, 55(13): 2982-2996. [41] Jing Liu, Feng Ma, Yaojie Zhang. Forecasting the Chinese Stock Volatility Across Global Stock Markets. Physica A: Statistical Mechanics and Its Applications, 2019, 525: 466-477. [42] Jing Liu, Feng Ma, Yingkai Tang, et al. Geopolitical Risk and Oil Volatility: A New Insight. Energy Economics, 2019, 84: 104548. [43] Weiju Xu, Feng Ma, Wang Chen, et al. Asymmetric Volatility Spillovers Between Oil and Stock Markets: Evidence from China and the United States. Energy Economics, 2019, 80: 310-320. [44] Weiju Xu, Jiqian Wang, Feng Ma, et al. Forecast the Realized Range-based Volatility: The Role of Investor Sentiment and Regime Switching. Physica A: Statistical Mechanics and its Applications, 2019, 527: 121422. [45] Yanyan Xu, Dengshi Huang, Feng Ma, et al. Liquidity and Realized Range-based Volatility Forecasting: Evidence from China. Physica A: Statistical Mechanics and Its Applications, 2019, 525: 1102-1113. [46] Yanyan Xu, Dengshi Huang, Feng Ma, et al. The Heterogeneous Impact of Liquidity on Volatility in Chinese Stock Index Futures Market. Physica A: Statistical Mechanics and Its Applications, 2019, 517: 73-85. [47] Yaojie Zhang, Feng Ma, Bo Zhu. Intraday Momentum and Stock Return Predictability: Evidence from China. Economic Modelling, 2019, 76(1): 319-329. [48] Yaojie Zhang, Feng Ma, Tianyi Wang, et al. Out‐of‐sample Volatility Prediction: A New Mixed‐frequency Approach. Journal of Forecasting, 2019, 38(7): 669-680. [49] Yaojie Zhang, Feng Ma, Yu Wei. Out-of-sample Prediction of the Oil Futures Market Volatility: A Comparison of New and Traditional Combination Approaches. Energy Economics, 2019, 81: 1109-1120. [50] Yaojie Zhang, Feng Ma, Yudong Wang. Forecasting Crude Oil Prices with A Large Set of Predictors: Can Lasso Select Powerful Predictors? Journal of Empirical Finance, 2019, 54: 97-117. [51] Yaojie Zhang, Qing Zeng, Feng Ma, Benshan Shi. Forecasting Stock Returns: Do Less Powerful Predictors Help? Economic Modelling, 2019, 78: 32-39. [52] Yaojie Zhang, Yu Wei, Feng Ma, et al. Economic Constraints and Stock Return Predictability: A New Approach. International Review of Financial Analysis, 2019, 63: 1-9. [53] Yixiang Chen, Feng Ma, Yaojie Zhang. Good, Bad Cojumps and Volatility Forecasting: New Evidence from Crude Oil and The US Stock Markets. Energy Economics, 2019, 81: 52-62. [54] Yongsheng Yi, Feng Ma, Dengshi Huang, et al. Interest Rate Level and Stock Return Predictability. Review of Financial Economics, 2019, 37(4): 506-522. [55] Yongsheng Yi, Feng Ma, Yaojie Zhang, Dengshi Huang. Forecasting Stock Returns with Cycle-decomposed Predictors. International Review of Financial Analysis, 2019, 64: 250-261. [56] Yu Li, Feng Ma, Yaojie Zhang, et al. Economic Policy Uncertainty and the Chinese Stock Market Volatility: New Evidence. Applied Economics, 2019, 51(49): 5398-5410. [57] Feng Ma, Jing Liu, M.I.M.Wahab, et al. Forecasting the Aggregate Oil Price Volatility in A Data-rich Environment. Economic Modelling, 2018, 72: 320-332. [58] Feng Ma, M.I.M.Wahab, Jing Liu, et al. Is Economic Policy Uncertainty Important to Forecast the Realized Volatility of Crude Oil Futures? Applied Economics, 2018, 50(18): 2087-2101. [59] Feng Ma, Yaojie Zhang, Dengshi Huang, Xiaodong Lai. Forecasting Oil Futures Price Volatility: New Evidence from Realized Range-based Volatility. Energy Economics, 2018, 75: 400-409. [60] Feng Ma, Yu Li, Li Liu, et al. Are Low-frequency Data Really Uninformative? A Forecasting Combination Perspective. The North American Journal of Economics and Finance, 2018, 44: 92-108. [61] Feng Ma, Yu Wei, Li Liu, Dengshi Huang. Forecasting Realized Volatility of Oil Futures Market: A New Insight. Journal of Forecasting, 2018, 37(4): 419-436. [62] Feng Ma, Yu Wei, Wang Chen, et al. Forecasting the Volatility of Crude Oil Futures Using High-frequency Data: Further Evidence. Empirical Economics, 2018, 55(2): 653-678. [63] Jing Liu, Feng Ma, Ke Yang, et al. Forecasting the Oil Futures Price Volatility: Large Jumps and Small Jumps. Energy Economics, 2018, 72: 321-330. [64] Lu Wang, Rong Zhang, Lin Yang, Yang Su, Feng Ma. Pricing Geometric Asian Rainbow Options Under Fractional Brownian Motion. Physica A: Statistical Mechanics and Its Applications, 2018, 494: 8-16. [65] Yaojie Zhang, Feng Ma, Benshan Shi, Dengshi Huang. Forecasting the Prices of Crude Oil: An Iterated Combination Approach. Energy Economics, 2018, 70: 472-483. [66] Yongsheng Yi, Feng Ma, Yaojie Zhang, Dengshi Huang. Forecasting the Prices of Crude Oil Using the Predictor, Economic and Combined Constraints. Economic Modelling, 2018, 75: 237-245. [67] Yudong Wang, Li Liu, Feng Ma, et al. Momentum of Return Predictability. Journal of Empirical Finance, 2018, 45: 141-156. [68] Dexiang Mei, Jing Liu, Feng Ma, et al. Forecasting Stock Market Volatility: Do Realized Skewness and Kurtosis Help? Physica A: Statistical Mechanics and Its Applications, 2017, 481: 153-159. [69] Feng Ma, Jing Liu, Dengshi Huang, et al. Forecasting the Oil Futures Price Volatility: A New Approach. Economic Modelling, 2017, 64: 560-566. [70] Feng Ma, M.I.M.Wahab, Dengshi Huang, et al. Forecasting the Realized Volatility of the Oil Futures Market: A Regime Switching Approach. Energy Economics, 2017, 67: 136-145. [71] Jing Liu, Yu Wei, Feng Ma, et al. Forecasting the Realized Range-based Volatility Using Dynamic Model Averaging Approach. Economic Modelling, 2017, 61: 12-26. [72] Zhicao Liu, Yong Ye, Feng Ma, et al. Can Economic Policy Uncertainty Help to Forecast the Volatility: A Multifractal Perspective. Physica A: Statistical Mechanics and Its Applications, 2017, 482: 181-188. [73] Ma Feng, Wei Yu, Huang Dengshi. Forecasting the Realized Volatility Based on the Signed Return and Signed Jump Variation. Journal of Management Science, 2017, 20(10): 31-43. |
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