Roy H. Kwon
Roy H. Kwon is a Professor at the Department of Mechanical and Industrial Engineering at the University of Toronto, St. George Campus. He is also a member of the faculty in the Masters of Mathematical Finance (MMF) Program at the University of Toronto. He received his PhD from the University of Pennsylvania in Operations Research from the Department of Electrical and Systems Engineering in 2002. His research focuses on financial engineering (portfolio optimization, asset allocation, risk management, and option pricing). In addition, he has worked and consulted in the use of operations research (optimization) for the financial and service sectors.
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Articles by Roy H. Kwon
Distributionally robust optimization approaches to credit risk management of corporate loan portfolios
A new approach to manage credit risk in financial institutions - the empirical divergence-based distributionally robust optimization - is proposed and shown to alleviate the challenges of sample sparsity and data uncertainty in credit risk modeling.
An effective credit rating method for corporate entities using machine learning
The authors propose a new method to design credit risk rating models for corporate entities using a meta-algorithm which exploits information embedded in expert-assigned credit ratings to rank customers.
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
A regime-switching factor model for mean–variance optimization
In this paper the authors formulate a novel Markov regime-switching factor model to describe the cyclical nature of asset returns in modern financial markets.
A shrinking horizon optimal liquidation framework with lower partial moments criteria
In this paper, a novel quasi-multiperiod model for optimal position liquidation in the presence of both temporary and permanent market impact is proposed. Two main features distinguish the proposed approach from its alternatives.