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- Title
Data-driven portfolio management with quantile constraints.
- Authors
Çetinkaya, Elçin; Thiele, Aurélie
- Abstract
We investigate an iterative, data-driven approximation to a problem where the investor seeks to maximize the expected return of her portfolio subject to a quantile constraint, given historical realizations of the stock returns. The approach, which was developed independently from Calafiore (SIAM J Optim 20:3427-3464 ) but uses a similar idea, involves solving a series of linear programming problems and thus can be solved quickly for problems of large scale. We compare its performance to that of methods commonly used in the finance literature, such as fitting a Gaussian distribution to the returns (Keisler, Decision Anal 1:177-189 ; Rachev et al. Advanced stochastic models, risk assessment and portfolio optimization: the ideal risk, uncertainty and performance measures, Wiley, New York ). We also analyze the resulting efficient frontier and extend our approach to the case where portfolio risk is measured by the inter-quartile range of its return. Our main contribution is in the detail of the implementation, i.e., the choice of the constraints to be generated in the master problem, as well as the numerical simulations and empirical tests, and the application to the inter-quartile range as a risk measure.
- Subjects
GAUSSIAN distribution; CAPITAL investments; RATE of return; INVESTMENTS; INVESTORS; FINANCIAL performance
- Publication
OR Spectrum, 2015, Vol 37, Issue 3, p761
- ISSN
0171-6468
- Publication type
Article
- DOI
10.1007/s00291-015-0396-9