Yunshen Yang is a Scientia PhD candidate and a teaching fellow in the School of Risk and Actuarial Studies, he is also the student representative for 2021. Yunshen holds a BSc in Statistics and a MSc in Management Science and Engineering from the University of Science and Technology of China where he is admitted in the Special Class for Gifted Yound.
Yunshen is teaching Life Insurance and Superannuation (ACTL3151/5105) and Asset-Liability and Derivative Models and is very passionate about leading students to think actively and deeply. He always encourages students to practice their logic thinking and build connections within different concepts that they have learned before.
Personal Website: https://sites.google.com/view/yunshen-alex-yang/home
PhD Teaching Fellowship
During my study in China, I mainly studied the topic of online financing. Specifically, I study the customer behavior and mechanism design on crowdfunding platforms and published a paper regarding how to allocate profits properly in investment-based crowdfunding to improve success rates.
I am currently applying model uncertainty to actuarial problems to produce robust estimates and insights for the industry. I am also interested in large portfolio losses and systemic risk in insurance and want to conduct more research in these areas. Moreover, I am also collaborating with scholars from behavioral finance to study how people's learning behavior when making financial decisions under different types of uncertainty.
Publications and Projects:
Yang, Y., Bi, G. and Liu, L., 2020. Profit allocation in investment-based crowdfunding with investors of dynamic entry times. European Journal of Operational Research, 280(1), pp.323-337.
Tang, Q., Yang, Y., 2022. Distributionally worst-case moments under Partial Ambiguity. Under Revision.
Tang, Q., Yang, Y., Yang, Y., 2022. High-quality Credit Portfolios under the Interplay of Common Shock and Systematic Risk. Under Submission.
Avanzi, B., Tang, Q., Wong, B., Yang, Y., 2022. Distributionally worst-case estimation under a varying extent of Ambiguity. Working.
Payzan-LeNestour, E., Tang, Q., Yang, Y., 2022. Learning about Regime Shifts through Pattern Recognition under uncertainty? An experimental study. Working.