Lei (Larry) Hua, Ph.D., ASA

Associate Professor

Lei Hua works in quantitative risk management and develops statistical and actuarial methods for analyzing complex, high-dimensional and interdependent risks across insurance, finance, environmental systems and cybersecurity applications.

Research

Hua develops statistical and actuarial methods for quantitative risk management, centered on copula theory — especially full-range tail dependence copulas, tail order and factor copula models — and their applications to insurance, finance and cybersecurity risk. His contributions to tail comonotonicity and hidden regular variation provide tools for assessing high-dimensional risks in extreme scenarios, and his work extends to cybersecurity insurance pricing, pension mortality modeling and financial machine learning. His 2019 paper received the North American Actuarial Journal Annual Best Paper Award. He has been supported by the Society of Actuaries, the Casualty Actuarial Society and Argonne National Laboratory, has published in Insurance: Mathematics and Economics, ASTIN Bulletin and the Journal of Multivariate Analysis, and has developed the R packages CopulaOne and fmlr.

Education

Ph.D., University of British Columbia — Actuarial Science, Quantitative Risk Management, and Statistical Modeling

Selected Recent Publications

Hua, L., 2023. Discovering Intraday Tail Dependence Patterns via a Full-Range Tail Dependence Copula. Risks, 11(11), 195.

Hua, L. and Xu, M., 2021. Pricing Cyber Insurance for a Large-Scale Network. Variance, 14(2).

Su, J. and Hua, L., 2017. A General Approach to Full-range Tail Dependence Copulas. Insurance: Mathematics and Economics, 77, 49-64.