About Us

The missions of the Center for Mathematical Assessment of Risks include study of the theory and application of quantitative finance, building a platform to exchange ideas between different disciplines of finance, and promoting direct cooperation between academic research and finance industries.

CMAR is also a research center for new theories of mathematical finance. We also train and teach quantitative finance to both academic and financial industry personnel. The center is under the leadership of Shige Peng. Director Peng developed the Backward Stochastic Differential Equations theory, which laid a theoretical foundation for the dynamical hedging practices in the financial industry. Furthermore, Director Peng also developed a theory of Nonlinear G-Expectations, which has wide application to the evaluation of financial risks. Nonlinear G-expectation is the only consistent theory which is capable of placing probability theory, model uncertainty, and fat-tail phenomena into one apparatus.

Since the establishment of CMAR in January 2015, it has successfully organized two major workshops and many small meetings. A hands-on problems solving workshop was organized in April 2015 and 42 graduate students were able to experience a realistic industrial financial simulation: six quantitative finance problems were proposed from financial industries from China and abroad and each group of students had to develop reports and write computer programs during the five day workshop. The center also organized an accredited Summer school program in July 2015 wherein more than 80 graduate students enrolled for the latest instruction in mathematical finance.

CMAR is building extensive connections with other research centers in China and abroad. In addition, we are collaborating with banks, the insurance industry, and financial securities companies.

Committee Members

Shige Peng

Academician of the Chinese Academy of Sciences

Lihe Wang
Executive director

Professor of Mathematics, University of Iowa

Research Area

 •General Theory of Non-Linear Risk and Uncertainty Management
 •Systemic Risk and Capital Allocation: Central Clearing Houses Risk Management
 •Quantification of Risk and Implementation. Compliance to Basel Regulation
 •Dynamic Portfolio Allocation Under Model Uncertainty