Organized by Kees Oosterlee
13:30 -- 14:10 Prof.dr. Zili Zhu, Director of RiskLab Data61 of CSIRO
(Commonwealth Scientific & Industrial Research Organisation of Australia)
Optimal Decisions-Making in Retirement Life-cycle Management
14:15 -- 14:45 Tim den Haan, Techn. Univ. Delft
On Dynamic Life Cycle pension plans
14:45 -- 15:15 Coffee, tea
15:15 -- 15:45 Dr. Martin van der Schans, Ortec Finance
Near Optimal Portfolio Construction
15:45 -- 16:25 Prof.dr. Duan Li, City Univ. Hong Kong
Mean-Variance Induced Utility Maximization Framework: Risk and Potential
Space is limited so please register for this workshop, thank you!
==========================================
Abstracts Talks 2 and 3:
On Dynamic Life Cycle pension plans, Tim den Haan
Abstract: This thesis studies the asset allocation of a DC pension investor over a long time horizon. Investors allocate their portfolio wealth between two assets: a return portfolio and a matching portfolio. Investors can adjust their allocation once a year. Several dynamic investment strategies that improve investment results compared to fixed allocations or static life cycles are shown. The dynamic investment strategies have been constructed by using two different approaches. The first approach is rule-based and defines intermediate wealth targets for every year in the investment horizon. Investment decisions are taken based on performance compared to these targets. The second approach involves a dynamic programming algorithm. The asset allocation over time is not always stable when using dynamic programming. Methods to smooth the asset allocation over time and improve stability are discussed. Last, both approaches are combined in one strategy.
Near-optimal portfolio construction, Dr. Martin van der Schans
Many investors use optimization to determine their optimal investment portfolio. Unfortunately, optimal portfolios are sensitive to changing input parameters, i.e., they are not robust. Traditional robust optimization approaches aim for an optimal and robust portfolio which, ideally, is the final investment decision. In practice, however, portfolio optimization supports but seldomly replaces the investment decision process. We present an approach that both solves the robustness problem and aims to support rather than replace the investment decision process. The method determines a region with near-optimal portfolios which, especially in light of the robustness problem, are all good allocation decisions. Then, as is already common practice, an investor can bring in expert opinion or additional information to select a preferred near-optimal portfolio. We will show that the region of near-optimal portfolios is significantly more robust than the optimal portfolio itself.
https://www.ortecfinance.com/en/insights/research/robust-optimization-by-constructing-near-optimal-portfolios
https://www.ortecfinance.com/en/insights/research/near-optimal-portfolio-construction
