Introduction to Linear Optimization
Introduction to Linear Optimization
Operations Research
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
SIAM Journal on Optimization
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
SIAM Journal on Optimization
Journal of Computational and Applied Mathematics
| Hi-index | 22.14 |
This paper proposes a novel methodology for optimal allocation of a portfolio of risky financial assets. Most existing methods that aim at compromising between portfolio performance (e.g., expected return) and its risk (e.g., volatility or shortfall probability) need some statistical model of the asset returns. This means that: (i) one needs to make rather strong assumptions on the market for eliciting a return distribution, and (ii) the parameters of this distribution need be somehow estimated, which is quite a critical aspect, since optimal portfolios will then depend on the way parameters are estimated. Here we propose instead a direct, data-driven, route to portfolio optimization that avoids both of the mentioned issues: the optimal portfolios are computed directly from historical data, by solving a sequence of convex optimization problems (typically, linear programs). Much more importantly, the resulting portfolios are theoretically backed by a guarantee that their expected shortfall is no larger than an a-priori assigned level. This result is here obtained assuming efficiency of the market, under no hypotheses on the shape of the joint distribution of the asset returns, which can remain unknown and need not be estimated.