Ruodu Wang

Professor / University Research Chair / Associate Chair - Research / SunLife Research Fellow

Ruodu Wang
Contact Information:
Office: M3 3122
Phone: 519-888-4567, ext. 31569

Ruodu Wang's personal website

Research interests

Professor Wang's research interests mainly lie in quantitative risk management, which includes various topics in actuarial science, financial engineering, operations research, probability, statistics, and economic theory.His current specific research topics include:

  • Risk, ambiguity, and uncertainty
  • Risk measures
  • Risk aggregation
  • Dependence modeling and copulas
  • Probabilistic combinatorics
  • Non-convex optimization
  • Optimal transport
  • Portfolio selection and diversification
  • Risk sharing
  • Decision analysis and behavioral economics
  • FinTech/InsurTech
  • Capital allocation
  • Robust methods for finance
  • E-values and e-inference
  • Multi-armed bandit
  • Selective inference


  • 2012 PhD (Mathematics) Georgia Institute of Technology, U.S.A. Advisor: Liang Peng.
  • 2009 MS (Financial Mathematics) Peking University, China.
  • 2006 BS (Mathematics) Peking University, China.

Professor Wang works in the Department of Statistics and Actuarial Science at the University of Waterloo as Assistant Professor (2012 - 2017), Associate Professor (2017 - 2022), Professor (2022 – present), University Research Chair (2018 - present), and Sun Life Fellow (2021 – present).  He serves as a Co-Editor of the European Actuarial Journal (2016 - present) and a Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association (2018 - present), and is an affiliated member of RiskLab at ETH Zurich, Switzerland (2015 - present). Among other international awards, he is the inaugural winner of the SOA Actuarial Science Early Career Award in 2021 from the Society of Actuaries (2021) and an elected Fellow of the Institute of Mathematical Statistics (2022).

Selected publications

  • Castagnoli, E., Cattelan, G., Maccheroni, F., Tebaldi, C. and Wang, R. (forthcoming). Star-shaped risk measures. Operations Research.
  • Li, H. and Wang, R. (forthcoming). PELVE: Probability equivalent level of VaR and ES. Journal of Econometrics.
  • Wang, R. and Ramdas, A. (forthcoming). False discovery rate control with e-values. Journal of the Royal Statistical Society Series B.
  • Liu, F., Mao, T., Wang, R. and Wei, L. (forthcoming). Inf-convolution, optimal allocations, and model uncertainty for tail risk measures. Mathematics of Operations Research.
  • Vovk, V., Wang, B. and Wang, R. (2022). Admissible ways of merging p-values under arbitrary dependence. Annals of Statistics, 50(1), 351–375.
  • Nutz, M. and Wang, R. (2022). The directional optimal transport. Annals of Applied Probability, 32(2), 1400–1420.
  • Chen, Y., Liu, P., Liu, Y. and Wang, R. (2022). Ordering and inequalities for mixtures on risk aggregation. Mathematical Finance, 32(1), 421–451.
  • Embrechts, P., Schied, A. and Wang, R. (2022). Robustness in the optimization of risk measures. Operations Research, 70(1), 95–110.
  • Burzoni, M., Munari, C. and Wang, R. (2022). Adjusted Expected Shortfall. Journal of Banking and Finance, 134, 106297.
  • Wang, R. and Ziegel, J. (2021). Scenario-based risk evaluation. Finance and Stochastics, 25, 725–756.
  • Embrechts, P., Mao, T., Wang, Q. and Wang, R. (2021). Bayes risk, elicitability, and the Expected Shortfall. Mathematical Finance, 31(4), 1190–1217.
  • Xu, Z., Wang, R. and Ramdas, A. (2021). A unified framework for bandit multiple testing. Advances in Neural Information Processing Systems (NeurIPS 2021), 16833–16845.
  • Liu, P., Schied, A. and Wang, R. (2021). Distributional transforms, probability distortions, and their applications. Mathematics of Operations Research. 46(4), 1490–1512.
  • Liu, F. and Wang, R. (2021). A theory for measures of tail risk. Mathematics of Operations Research, 46 (3), 1109–1128.
  • Vovk, V. and Wang, R. (2021). E-values: Calibration, combination, and applications. Annals of Statistics, 49 (3), 1736–1754.
  • Wang, R. and Zitikis, R. (2021). An axiomatic foundation for the Expected Shortfall. Management Science, 67 (3), 1413–1429.
  • Vovk, V. and Wang, R. (2020). Combining p-values via averaging. Biometrika, 107 (4), 791–808.
  • Wang, R. and Wei, Y. (2020). Risk functionals with convex level sets. Mathematical Finance, 30 (4), 1337–1367.
  • Wang, R., Wei, Y. and Willmot, G. (2020). Characterization, robustness and aggregation of signed Choquet integrals. Mathematics of Operations Research, 45 (3), 993–1015.
  • Embrechts, P., Liu, H., Mao, T. and Wang, R. (2020). Quantile-based risk sharing with heterogeneous beliefs. Mathematical Programming, 181 (2), 319–347.
  • Mao, T. and Wang, R. (2020). Risk aversion in regulatory capital principles. SIAM Journal on Financial Mathematics, 11 (1), 169–200.
  • Wang, R., Xu, Z. Q. and Zhou, X. Y. (2019). Dual utilities under dependence uncertainty. Finance and Stochastics, 23(4), 1025-1048.
  • Asimit, V., Peng, L., Wang, R. and Yu, A. (2019). An efficient approach to quantile capital allocation and sensitivity analysis. Mathematical Finance, 29(4), 1131-1156.
  • Shen, J., Shen, Y., Wang, B. and Wang, R. (2019). Distributional compatibility for change of measures. Finance and Stochastics, 23(3), 761-794.
  • Embrechts, P., Liu, H. and Wang, R. (2018). Quantile-based risk sharing. Operations Research, 66(4), 936-949.
  • Li. L., Shao, H., Wang, R. and Yang, J. (2018). Worst-case Range Value-at-Risk with partial information. SIAM Journal on Financial Mathematics, 9(1), 190-218.
  • Cai, J., Liu, H. and Wang, R. (2018). Asymptotic equivalence of risk measures under dependence uncertainty. Mathematical Finance, 28(1), 29-49.
  • Furman, E., Wang, R. and Zitikis, R. (2017). Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks. Journal of Banking and Finance, 83, 70-84.
  • Bernard, C., Rüschendorf, L., Vanduffel, S. and Wang, R. (2017). Risk bounds for factor models. Finance and Stochastics, 21(3), 631-659.
  • Wang, B. and Wang, R. (2016). Joint mixability. Mathematics of Operations Research, 41(3), 808-826.
  • Embrecths, P., Hofert, M. and Wang, R. (2016). Bernoulli and tail-dependence compatibility. Annals of Applied Probability, 26(3), 1636-1658.
  • Puccetti, G. and Wang, R. (2015). Extremal dependence concepts. Statistical Science, 30(4), 485-517.
  • Embrechts, P., Wang, B. and Wang, R. (2015). Aggregation-robustness and model uncertainty of regulatory risk measures. Finance and Stochastics, 19(4), 763-790.
  • Wang, R., Bignozzi, V. and Tsanakas, A. (2015). How superadditive can a risk measure be? SIAM Journal on Financial Mathematics, 6(1), 776-803.
  • Bernard, C., Jiang, X. and Wang, R. (2014). Risk aggregation with dependence uncertainty. Insurance: Mathematics and Economics, 54, 93-108.
  • Zhang, R., Peng, L. and Wang, R. (2013). Tests for covariance matrix with fixed or divergent dimension. Annals of Statistics, 41(4), 2075-2096.
  • Wang, R., Peng, L. and Yang, J. (2013). Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities. Finance and Stochastics, 17(2), 395-417.
  • Wang, B. and Wang, R. (2011). The complete mixability and convex minimization problems for monotone marginal distributions. Journal of Multivariate Analysis, 102(10), 1344-1360.