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Index Replication using Portfolio Optimization Methods
Replicate the payouts (profits and losses (P&L)) of a certain stock market index (i.e., a portfolio of stocks) with only a limited number of liquid (i.e., highly traded) stocks. Index replication is an important tool for portfolio managers seeking returns of indices that are not directly investable for most people by mimicking the performance of such indices with investable stocks.
Write an optimization program which, given a set of stocks, maximizes a similarity measure to a pre- defined index. One of the main challenges lies in the definition of the objective function such that one gets a payoff which is as close as possible to that of the index. Compare various approaches in terms of speed and accuracy (from a computational as well as an economic perspective).
Stock market data will be provided.
There are several ways how the replication can be done. Depending on the choice of method / objective function, the optimization program is more or less complicated (from quadratic to highly non-linear) and correspondingly, the complexity of the solver method can vary from easy to challenging.
The following links provide an overview of some potential approaches:
Index Replication using Portfolio Optimization Methods | by Jason Yip | Towards Data Science.
Index Replication: Principles And Applications | Lipper Alpha Insight | Refintiv (refinitiv.com).
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Bachelard Cyril <Cyril.Bachelard at olz.ch> He is a Senior Quantitative Research Analyst in OLZ AG since 2011. He is an expert on Quantitative Research, risk forecasting, and optimization models. He is also a Ph.D. student in Algorithmic Sampling and Portfolio Optimization at the University of Lausanne. He holds a master's degree in economics and has further completed studies in mathematics, statistics, and computer science.
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Apostolos Chalkis <tolis.chal at gmail.com> is an expert in statistical software, computational geometry, and optimization, and has previous GSoC student experience (2018 & 2019) and mentoring experience with GeomScale (2020 & 2021).
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Vissarion Fisikopoulos <vissarion.fisikopoulos at gmail.com> is an international expert in mathematical software, computational geometry, and optimization, and has previous GSOC mentoring experience with Boost C++ libraries (2016-2017) and the R-project (2017).