Before revision: We investigate two methods for reducing estimation error in portfolio optimization with Conditional Value-at-Risk (CVaR ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.
After revision: We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the available log-return data is iid, and detail the approach for two cases: nonparametric and parametric (the log-return distribution belongs in the elliptical family ). The first method is nonparametric : penalize portfolios with large variances in mean and CVaR estimations.