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Dr Weining Wang

Reader in Economics

School of Arts and Social Sciences, Department of Economics

Contact Information


Visit Weining Wang

D310, Rhind Building

Postal Address

City, University of London
Northampton Square
United Kingdom



Dr. Weining Wang's research interest is on financial econometrics. In particular she specializes in non and semi-parametrics statistics, network anlaysis, high dimensional nonstationary time series.


Journal articles (7)

  1. Zhu, X., Wang, W., Wang, H. and Härdle, W.K. (2019). Network quantile autoregression. Journal of Econometrics, 212(1), pp. 345–358. doi:10.1016/j.jeconom.2019.04.034.
  2. Fan, Y., Härdle, W.K., Wang, W. and Zhu, L. (2018). Single-Index-Based CoVaR With Very High-Dimensional Covariates. Journal of Business & Economic Statistics, 36(2), pp. 212–226. doi:10.1080/07350015.2016.1180990.
  3. Härdle, W.K., Wang, W. and Yu, L. (2016). TENET: Tail-Event driven NETwork risk. Journal of Econometrics, 192(2), pp. 499–513. doi:10.1016/j.jeconom.2016.02.013.
  4. Wang, (2016). Localising temperature risk. Journal of the American Statistical Association.
  5. Härdle, W.K., Okhrin, O. and Wang, W. (2015). HIDDEN MARKOV STRUCTURES FOR DYNAMIC COPULAE. Econometric Theory, 31(5), pp. 981–1015. doi:10.1017/s0266466614000607.
  6. Hardle, W.K., Okhrin, Y. and Wang, W. (2015). Uniform Confidence Bands for Pricing Kernels. Journal of Financial Econometrics, 13(2), pp. 376–413. doi:10.1093/jjfinec/nbu002.
  7. Spokoiny, V., Wang, W. and Karl Härdle, W. (2013). Local quantile regression. Journal of Statistical Planning and Inference, 143(7), pp. 1109–1129. doi:10.1016/j.jspi.2013.03.008.

Reports (3)

  1. Cui, W., Hardle, W.K. and Wang, W. (2016). Estimation of NAIRU with In ation Expectation Data. London, UK: City, University of London.
  2. Zbonakova, L., Hardle, W.K. and Wang, W. (2016). Time Varying Quantile Lasso. London, UK: City, University of London.
  3. Chen, S., Hardle, W.K. and Wang, W. (2016). Inflation Co-movement across Countries in Multi-maturity
    Term Structure: An Arbitrage-Free Approach.
    London, UK: City, University of London.

Scholarly edition

  1. Asgharian, H., Christiansen, C., Hou, A.J. and Wang, W. (2017). Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing.

Working paper

  1. Chernozhukov, V., Hardle, W.K., Huang, C. and Wang, W. (2018). LASSO-Driven Inference in Time and Space. London, UK: Department of Economics, City, University of London.