ABOUT ME

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Welcome to visit my homepage. I am Guangyu Mao /'gwɑŋ'jo mau/ (卯光宇), an associate professor of Finance at Beijing Jiaotong University (BJTU). I received my Ph.D. in Finance from Peking University in 2013 and then joined the department of finance in the School of Economics and Management of BJTU. My research concentrates primarily on econometric and statistical theories. Besides, I am also interested in empirical finance and macroeconomics.

Contact

Email:
Office: Room 926, Science and Technology Building, 3 Shang Yuan Cun, Hai Dian District, Beijing, P. R. China (100044)

Education

Ph.D. in Finance, National School of Development, Peking University, China, 2013
B.Sc. in Chemistry, College of Chemistry and Molecular Engineering, Peking University, China, 2007
B.A. in Economics, China Center for Economic Research, Peking University, China, 2007

Employment

Associate Professor, Department of Finance, School of Economics and Management, Beijing Jiaotong University (2016 - Present)
Assistant Professor, Department of Finance, School of Economics and Management, Beijing Jiaotong University (2013 - 2016)

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RESEARCH

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Research Interests

My current research interests mainly focus on the following topics:

• Modelling with high-frequency financial data
• Modelling based on extreme value theories
• Testing for cross-sectional dependence in large-dimensional panel data models
• Testing for covariance structures in high dimensions
• Bayesian modelling of nonlinear spatial econometric models

Publications

• Mao, G. and Y. Shen (2019). Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence, China Economic Review, Vol. 53, 53-63.
• Mao, G. (2019). On high-dimensional tests for mutual independence based on Pearson's correlation coefficient, forthcoming in Communications in Statistics - Theory and Methods.
• Mao, G. and Z. Zhang (2018). Stochastic tail index model for high frequency financial data with Bayesian analysis, Journal of Econometrics, Vol. 205, 470-487.
• Mao, G. (2018). Testing for sphericity in a two-way error components panel data model, Econometric Reviews, Vol. 37, 491-506.
• Mao, G. (2018). Testing independence in high dimensions using Kendall's tau, Computational Statistics and Data Analysis, Vol. 117, 128-137.
• Mao, G. (2018). Testing for error cross-sectional uncorrelatedness in a two-way error components panel data model, Communications in Statistics - Theory and Methods, Vol. 47, 4808-4839.
• Mao, G. (2017). Variance-corrected tests for covariance structures with high-dimensional data, Journal of Multivariate Analysis, Vol. 162, 71-81.
• Mao, G. (2017). Robust test for independence in high dimensions, Communications in Statistics - Theory and Methods, Vol. 46, 10036-10050.
• Mao, G. (2016). Testing for error cross-sectional independence using pairwise augmented regressions, Econometrics Journal, Vol. 19, 237-260.
• Mao, G. (2016). A note on tests for high-dimensional covariance matrices, Statistics and Probability Letters, Vol. 117, 89-92.
• Mao, G. (2016). Do regional house prices converge or diverge in China, China Economic Journal, Vol. 9, 154-166.
• Mao, G. (2015). A note on testing complete independence for high dimensional data, Statistics and Probability Letters, Vol. 106, 82-85.
• Mao, G. (2015). Efficient penalized estimation for linear regression model, Communications in Statistics - Theory and Methods, Vol. 44, 1436-1449.
• Mao, G. (2015). Model selection of M-estimation models using least squares approximation, Statistics and Probability Letters, Vol. 99, 238-243.
• Mao, G. (2014). A new test of independence for high-dimensional data, Statistics and Probability Letters, Vol. 93, 14-18.
• Mao, G. (2014). A note on tests of sphericity and cross-sectional dependence for fixed effects panel model, Economics Letters, Vol. 122, 311-313.
• Mao, G. (2014). Testing for joint significance in nonstationary binary choice model, Economics Letters, Vol. 122, 215-219.
• Mao, G. (2013). Model selection for regression with heteroskedastic and autocorrelated errors, Economics Letters, Vol. 118, 497-501.
• Mao, G. (2012). Technical evolution: The biases of technical change of China (in Chinese), Nankai Economic Studies, 2012 No. 5, 65-78.

Referee Activity

Journal of Econometrics; Biometrika; Journal of Business and Economic Statistics; Journal of Financial Econometrics; Computational Statistics and Data Analysis; Journal of Multivariate Analysis; Economics Letters; Statistics and Probability Letters; International Journal of Systems Science; Statistics; China Finance Review International; China Economic Journal; China Economic Quarterly (in Chinese).

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TEACHING

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Undergraduate

• Financial Econometrics
  SEM, Beijing Jiaotong University, Spring 2018 and Spring 2019
• Behavioral Finance
  SEM, Beijing Jiaotong University, Summer 2019
• Econometrics
  SEM, Beijing Jiaotong University, Spring 2014, Spring 2015, and Spring 2016
• Intermediate Econometrics
  CCER, Peking University, Fall 2008, Spring 2009, Fall 2009, and Spring 2010 (As a TA)

Graduate

• Fixed Income Securities
  SEM, Beijing Jiaotong University, Spring 2019
• Time Series Analysis
  SEM, Beijing Jiaotong University, Spring 2014, Spring 2016, and Spring 2018
• Applied Time Series Analysis
  SEM, Beijing Jiaotong University, Spring 2016, Spring 2018, and Spring 2019
• Statistics (MPAcc)
  SEM, Beijing Jiaotong University, Spring 2014
• Advanced Econometrics
  NSD, Peking University, Spring 2010 and Spring 2011 (As a TA)

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