Optimal Diversification in the Presence of Parameter Uncertainty for a Risk Averse Investor
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Publication:5250044
DOI10.1137/130942826zbMath1311.91170OpenAlexW3121834105MaRDI QIDQ5250044
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Publication date: 15 May 2015
Published in: SIAM Journal on Financial Mathematics (Search for Journal in Brave)
Full work available at URL: http://eprints.lse.ac.uk/61024/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_Content_Veraart%2C%20L_Optimal_diversification_Veraart_Optimal%20diversification_2016.pdf
parameter uncertaintyrisk aversionoptimal investmentpower utilitysparse portfolio\(L_1\)-norm constraint
Statistical methods; risk measures (91G70) Optimal stochastic control (93E20) Portfolio theory (91G10)
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Cites Work
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- Optimum consumption and portfolio rules in a continuous-time model
- High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: risk underestimation
- Continuity of the solution map in quadratic programs under linear perturbations
- Continuous-time stochastic control and optimization with financial applications
- Convex duality in constrained portfolio optimization
- Least angle regression. (With discussion)
- THE EFFECT OF ESTIMATION IN HIGH-DIMENSIONAL PORTFOLIOS
- Sparse and stable Markowitz portfolios
- A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
- Ideal spatial adaptation by wavelet shrinkage
- Portfolio Optimization Using Forward-Looking Information*
- Sparse Approximate Solutions to Linear Systems
- The Relaxed Investor with Partial Information
- Vast Portfolio Selection With Gross-Exposure Constraints
- Proximité et dualité dans un espace hilbertien
- Portfolio Selection with Transaction Costs
- The relaxed investor and parameter uncertainty
