Ruodu Wang

Professor / Canada Research Chair (Tier 1) in Quantitative Risk Management
Ruodu Wang

Contact Information:
Office: M3 3122
Phone: 519-888-4567, ext. 31569
Email: wang@uwaterloo.ca

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
  • E-values and e-processes
  • Dependence modeling and copulas
  • Optimal transport
  • Portfolio selection and diversification
  • Risk sharing
  • FinTech and InsurTech
  • Capital allocation
  • Robust finance
  • E-values and e-inference
  • Selective inference

Education/biography

  • 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 – 2023), Sun Life Fellow (2021 – 2023), and Canada Research Chair (2023 – present).  He serves as a Co-Editor of the European Actuarial Journal (2016 - present), a Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association (2018 - present) and Associate Editor of six academic journals, including Operations Research, Finance and Stochastics, Mathematics of Operations Research, Canadian Journal of Statistics, and Journal of Mathematical Economics. 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

  • Wang, R. and Zhang, Z. (2025+). Quadratic-form optimal transport. Mathematical Programming.
  • Shen, Y., Van Oosten, Z. and Wang, R. (2025+). Partial law invariance and risk measures. Management Science.
  • Mao, T., Wang, R. and Wu, Q. (2025+). Model aggregation for risk evaluation and robust optimization. Management Science.
  • Liu, H., Wang, B., Wang, R. and Zhuang, S. C. (2025+). Distorted optimal transport. Mathematics of Operations Research.
  • Wang, Q., Wang, R. and Ziegel, J. (2025+). E-backtesting. Management Science.
  • Han, X., Wang, Q., Wang, R. and Xia, J. (2025+). Cash-subadditive risk measures without quasi-convexity. Mathematics of Operations Research.
  • Blanchet, J., Lam, H., Liu, Y. and Wang, R. (2025). Convolution bounds on quantile aggregation. Operations Research, 73(5), 2761–2781
  • Pesenti, S., Wang, Q. and Wang, R. (2025). Optimizing distortion riskmetrics with distributional uncertainty. Mathematical Programming, 213, 51–106.
  • Han, X., Lin, L. and Wang, R. (2025). Diversification quotients: Quantifying diversification via risk measures. Management Science, 71(9), 7990–8006.
  • Principi, G., Wakker, P. and Wang, R. (2025). Anticomonotonicity for preference axioms: The natural counterpart to comonotonicity. Theoretical Economics, 20(3), 831–855.
  • Wang, R. and Zhang, Z. (2025). Simultaneous optimal transport. Transactions of the American Mathematical Society, 378(8), 5845–5898.
  • Wang, R. (2025). The only admissible way of merging arbitrary e-values. Biometrika, 112(2), asaf020.
  • Maccheroni, F., Marinacci, M., Wang, R. and Wu, Q. (2025). Risk aversion and insurance propensity. American Economic Review, 115(5), 1597–1649.
  • Fan, Y., Jiao, Z. and Wang, R. (2025). Testing the mean and variance by e-processes. Biometrika, 112(1), asae049.
  • Gasparin, M., Wang, R. and Ramdas, A. (2025). Combining exchangeable p-values. Proceedings of the National Academy of Sciences, 122(11), e2410849122.
  • Chen, Y., Embrechts, P. and Wang, R. (2025). An unexpected stochastic dominance: Pareto distributions, dependence, and diversification. Operations Research, 73(3), 1336–1344.
  • Guo, N., Kou, S., Wang, B. and Wang, R. (2025). A theory of credit rating criteria. Management Science, 71(4), 3583–3599.
  • Zhang, Z., Ramdas, A., Wang, R. (2024). On the existence of powerful p-values and e-values for composite hypotheses. Annals of Statistics, 52(5), 2241–2267.
  • Nutz, M., Wang, R. and Zhang, Z. (2024). Martingale transports and Monge maps. Annals of Applied Probability, 34(6), 5556–5577.
  • Koike, T., Lin, L. and Wang, R. (2024). Joint mixability and notions of negative dependence. Mathematics of Operations Research, 49(4), 2786–2802.
  • Ignatiadis, N., Wang, R. and Ramdas, A. (2024). E-values as unnormalized weights in multiple testing. Biometrika, 111(2), 417–439.
  • Li, H. and Wang, R. (2023). PELVE: Probability equivalent level of VaR and ES. Journal of Econometrics, 234(1), 353–370.
  • Castagnoli, E., Cattelan, G., Maccheroni, F., Tebaldi, C. and Wang, R. (2022). Star-shaped risk measures. Operations Research, 70(5), 2637–2654.
  • Wang, R. and Ramdas, A. (2022). False discovery rate control with e-values. Journal of the Royal Statistical Society Series B, 84(3), 822–852.
  • Nutz, M. and Wang, R. (2022). The directional optimal transport. Annals of Applied Probability, 32(2), 1400–1420.
  • Vovk, V., Wang, B. and Wang, R. (2022). Admissible ways of merging p-values under arbitrary dependence. Annals of Statistics, 50(1), 351–375.
  • Liu, F., Mao, T., Wang, R. and Wei, L. (2022). Inf-convolution, optimal allocations, and model uncertainty for tail risk measures. Mathematics of Operations Research, 47(3), 2494–2519.
  • 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.
  • Liu, P., Schied, A. and Wang, R. (2021). Distributional transforms, probability distortions, and their applications. Mathematics of Operations Research, 46(4), 1490–1512.
  • Embrechts, P., Mao, T., Wang, Q. and Wang, R. (2021). Bayes risk, elicitability, and the Expected Shortfall. Mathematical Finance, 31(4), 1190–1217.
  • Wang, R. and Ziegel, J. (2021). Scenario-based risk evaluation. Finance and Stochastics, 25, 725–756.
  • 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.
  • 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.
  • Vovk, V. and Wang, R. (2020). Combining p-values via averaging. Biometrika, 107 (4), 791–808.
  • Embrechts, P., Liu, H., Mao, T. and Wang, R. (2020). Quantile-based risk sharing with heterogeneous beliefs. Mathematical Programming, 181 (2), 319–347.
  • 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. and Wang, R. (2018). Quantile-based risk sharing. Operations Research, 66(4), 936-949.
Links
Contact information: Ruodu Wang