The Boosted Difference of Convex Functions Algorithm for Value-at-Risk Constrained Portfolio Optimization
By Marah-Lisanne Thormann, Phan Tu Vuong, Alain Zemkoho
Rating
1445
Battle Count: 24
Relevance
9/10
Highly relevant for portfolio optimization and risk management in quantitative trading, especially for institutions required to manage Value-at-Risk.
Implementation Complexity
7/10
Requires understanding of convex optimization and DC programming. Implementation is facilitated by provided Python code, but may be complex for large-scale applications.
Reproducibility
4/5
The paper provides detailed methodology, algorithms, and experimental setup. Code is available on GitHub, enhancing reproducibility.
About this paper
Methodology: Boosted Difference of Convex Functions Algorithm (BDCA). Problem types: Portfolio Optimization, Risk Management.
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