英文期刊

[1]Wang, Y., Ma, F., Wei, Y., et al. Forecasting realized volatility in a changing world: A dynamic model averaging approach. Journal of Banking & Finance, 2016, 64:136-149.

[2]Pu, W., Chen, Y., Ma, F. Forecasting the realized volatility in Chinese stock market: further evidence. Applied Economics, 2016, 48:3316-3330.

[3]Li, L., Ma, F., Wang, Y. Forecasting excess stock returns with crude oil market data. Energy Economics, 2015, 48: 316-324.

[4]Ma, F., Li, L., Liu, Z., et al. Forecasting realized volatility A Markov regime switching approach. Applied economic letters, 2015, 22(17): 1361-1365.

[5]Liu, Z., Ma, F., Wang, X., et al. Forecasting the Realized Volatility: the Role of Jumps. Applied economics letters, 2015,23:736-739.

[6]Ma, F., Zhang, Q., Chen, P., et al. Multifractal detrended cross-correlation analysis of the oil-dependent economies: Evidence from the West Texas intermediate crude oil and the GCC stock markets. Physica A: Statistical Mechanics and its Applications, 2014, 410: 154-166.

[7]Ma, F., Wei, Y., Huang, D., et al. Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility. Physica A: Statistical Mechanics and its Applications, 2014, 405: 171-180.

[8]Ma, F., Wei, Y., Huang, D. Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets. Physica A: Statistical Mechanics and its Applications, 2013, 392(7): 1659-1670.

[9]Ma, F., Wei, Y., Huang, D., et al. Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC. Physica A: Statistical Mechanics and its Applications, 2013, 392(21): 5356-5368.

[10]Wang, Y., Liu, L, Ma, F., Wu, C. What the investors need to know about forecasting oil market volatility. Energy Economics,2016,53:128-139.

[11]Zhuang, X., Wei, Y., Ma, F. Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price. Physica A: Statistical Mechanics and its Applications, 2015, 430: 101-113.

[12]Wang, Y., Liu, L, Ma, F., Wu, C. What the investors need to know about forecasting oil market volatility. Energy Economics, 2016,57:128-139.

主要中文期刊（含录用）

[1]魏宇，马锋，黄登仕. 基于多分形波动率测度的中国股市波动率预测模型研究及MCS检验，管理科学学报，2015，18（8）：61-72.

[2]马锋，魏宇，黄登仕. 基于vine copula方法的股市组合动态VaR测度及预测模型研究，系统工程理论与实践，2015，35(1)：26-36.(入选F5000)

[3]马锋，魏宇，黄登仕. 基于跳跃和符号跳跃变差的HAR-RV预测模型研究及其MCS检验，系统管理学报，2015，24（5）：700-710.

[4]马锋，魏宇，黄登仕，夏泽安. 基于马尔科夫状态转换和跳跃的高频波动率模型预测研究，系统工程，2016，1，10-16.

[5]蒲旺，陈鹏，马锋，黄登仕. 基于copula方法的国际原油价格和新能源公司股价动态尾部相关性分析，中国金融学，2015，17：44-55.

[6]马锋，魏宇，黄登仕. 高频波动率预测模型研究：基于符号收益和符号跳跃变差的视角，管理科学学报，2016，已录用，排队发表.

[7]徐伟举，马锋，魏宇. 基于不同跳跃检验下的高频波动率模型预测研究，系统工程，2015年，已录用，排队发表.

[8]马锋，魏宇，黄登仕. 隔夜收益率能提高高频波动率模型的预测能力吗？（2014年第十二届金融系统工程与风险管理国际年会，优秀论文,系统工程学报,已录用，排队发表）

工作论文

[1]Ma, F., Wei, Y., Huang, D. Forecasting the realized volatility of the future oil market: a new insight. Journal of Forecasting, 2016, revised.

[2]Ma, F., Wei, Y., Huang, D. Forecasting the volatility of crude oil futures using high-frequency data： Further evidence. Empirical Economics, 2015,revised and under review.

[3]Ma, F., Wei, Y., Huang, D. Is low-frequency data really uninformative? An empirical study based on forecast combination. Economic Modelling, 2015, revised and under review.

[4]Liu, J.,Wei, Y., Ma, F. Forecasting the realized range volatility of the future oil market using dynamic averaging and selection models. Economic Modelling, 2016, revised and under review.

[5]Ma, F.,Wahab, M.I.M.,Liu, J., Wei, Y.Is economic policy uncertainty important to forecast the realized volatility of crude oil futures? Applied Economics, 2015,under review

[6]Ma, F.,Wahab, M.I.M. What we will find about when we talk about forecasting the volatility with regimes? submitting to EJOR

[7]Xu, W., Ma, F., Wei, Y. Is investor sentiment really helpful to forecast the realized range-based volatility. Applied Economics Letters, 2016, under review

[8]Ma, F.Forecasting the realized volatility: Does regimes matter?submitting to Economics Letters.

[9]Chen, W., Ma, F. Forecasting of the realized volatility the role of high order moments. submitting to Economic modelling.

[10]Liu,Z.,Ye, Y.,Ma,F.Can economic policy uncertainty help to forecast the stock return volatility a multi-fractal perspective.Physica A, revised.

[10]Ma, F.,Chao, Y., Wei, Y.,Huang, D. Jump, Cojump and Variance Risk Premium.

[11]Ma, F.,Wang, Y., Wei, Y.,Huang, D. Forecasting the oil price volatility: Does the volatility of realized volatility matter?

[12]Ma,F., Chen, W. Realized volatility and economic policy uncertainty: a threshold perspective.

[13]马锋.已实现和已实现极差波动率预测模型研究：基于马尔科夫转换机制的研究视角.

[14]雷立坤，魏宇，马锋.基于MIDAS-GARCH的政治经济不确定与中国股市波动率预测研究.

[15]陈王，马锋.高频波动模型在中国股市中的风险预测能力研究.

[16]曹阳，魏宇，马锋.沪深股市共同跳跃检验及套利研究.

科研项目信息

[1]基于高频、超高频数据下的波动率模型预测研究，西南交通大学研究生创新实验实践项目（项目编号：YC201405118），主持人.

[2]金融危机下原油价格冲击与金融市场波动及其联动复杂性–基于多分形机制转换波动率模型和藤copula方法的研究，国家自然基金（项目编号：71371157），主研.

[3]基于混频技术和组合预测的资产复杂性相关性密度预测研究：短期冲击、长期影响与机制转换，国家自然基金（项目编号：7161145），主研.

参与学术会议

[1]2016年第十四届金融系统工程与风险管理国际年会，黑龙江哈尔滨, 2016. 报告论文：已实现和已实现极差波动率预测模型研究：基于马尔科夫转换机制的研究视角.

[2]2016大数据驱动的管理与决策研究学术研讨会，中国香港，香港中文大学.

[3]2015年第十三届金融系统工程与风险管理国际年会，安徽芜湖, 2015.

报告论文：高频波动率预测模型研究：基于符号收益和符号跳跃变差的视角

[4]2014年第十二届金融系统工程与风险管理国际年会，山西太原，2014，获优秀论文奖. 报告论文：隔夜收益率能提高高频波动率模型的预测能力吗？

[5]2014 China International Conference in Finance, 成都, 2014.

[6]19th Annual Workshop on Economic Science with Heterogeneous Interacting Agents, 天津，2014.报告论文：Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility.