Journal of Risk

An estimation-free, robust conditional value-at-risk allocation model

Carlos Jabbour, Javier F. Peña, Juan C. Vera, Luis F. Zuluaga


We propose a novel optimization model for risk-averse investors to obtain robust solutions for portfolio allocation problems. Unlike related models in the literature, no historical data or statistical estimation techniques are used to compute the parameters of the model. Instead, the parameters are directly obtained from current prices of options on the assets being considered. Furthermore, the model only requires the solution of a linear program. To find a robust portfolio, we minimize the portfolio’s worst case conditional value-at-risk over all asset return distributions that replicate the current option prices. The model addresses the main practical limitations associated with classical portfolio allocation techniques, namely, the high sensitivity to model parameters and the difficulty to obtain accurate parameters’ estimates. These characteristics, together with their linear programming formulation and the use of a coherent downside measure of risk, should be appealing to practitioners. We provide numerical experiments to illustrate the characteristics of the model.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here