CARTE Applied AI/DS Seminar: Uniform Pessimistic Risk and Optimal Portfolio with Prof. Jong-June Jeon (University of Seoul)

Wednesday, March 1, 2023

The Joint CARTE (University of Toronto) and University of Seoul Applied AI/DS seminar series welcomes Professor Jong-June Jeon.

Registration: Please register through this link.

Abstract: The optimality of allocating assets has been widely discussed with the theoretical analysis of risk measures. Pessimism is one of the most attractive approaches beyond the conventional optimal portfolio model, and the alpha-risk plays a crucial role in deriving a broad class of pessimistic optimal portfolios. However, estimating an optimal portfolio assessed by a pessimistic risk is still challenging due to the absence of an available estimation model and a computational algorithm. In this study, we propose a version of integrated $\alpha$-risk called the uniform pessimistic risk and the computational algorithm to obtain an optimal portfolio based on the risk.
Further, we investigate the theoretical properties of the proposed risk in view of three different approaches: multiple quantile regression, the proper scoring rule, and distributionally robust optimization.
Also, the uniform pessimistic risk is applied to estimate the pessimistic optimal portfolio models for the Korean stock market and compare the result of the real data analysis. It is empirically confirmed that the proposed pessimistic portfolio presents a more robust performance than others when the stock market is unstable.

Bio: I am an associate professor, department of statistics, University of Seoul. I worked in the Seoul Metropolitan Government as Director-General of Statistics & Data from 2017 to 2018. I have researched asymptotic of penalized regression models and efficient computation algorithms for deep learning with quantile regression. Currently, my main interest is developing a generation model for machine learning with a causal structure and distributionally robust optimization for fast converging reinforcement learning.

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