Portfolio Strategies under Scenario Optimization

This project concentrates on the scenario optimization method which does not need to make any assumption for the underlying asset distribution and directly incorporate such uncertainty into the objective or constraint functions through stochastic programming. The scenario optimization is performed under different parameters and constraints while Markowitz and Black-Litterman model are taken as the benchmarks to evaluate if the scenario optimization can outperform the traditional methods with the same input exchange-traded funds (ETF) data. The efficient frontier of the portfolio determined by the scenario optimization is shown as well to compare with the traditional methods. A hypothesis test is conducted to see whether we can efficiently map the scenario optimization to Black-Litterman model. RiskGrid Technologies will benefit from participation in the internship as the realization of the approach will be direcly used to improve the services for the customers of RiskGrid Open Eikon App. TO BE CONT'D

Intern: 
Chengwei Qin
Faculty Supervisor: 
Traian Pirvu
Province: 
Ontario
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Partner University: 
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