Yunshen Yang is a Ph.D. Candidate in the School of Risk&Actuarial Studies at UNSW Business School. His research, which is highly interdisciplinary, focuses on the model misspecification in insurance and finance. His diverse research interests include the impacts of model risk on loss estimation, financial decisions, portfolio management, risk management, and financial behaviors. He pays special attention to the extreme scenarios suffering from data scarcity, such as tail risk, which is of crucial importance to the financial system, especially during the current chaotic environment. He has collaborated with scholars from multiple disciplines including operations research, statistics, and behavioral finance, and has published in high-quality journals including the European Journal of Operational Research and ASTIN Bulletin.
Ph.D. Teaching Fellowship; Associate Fellowship of Higher Education Academy
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.