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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