报 告人：Nathan LassanceAssociate Professor of finance at UCLouvain in Belgium

报告时间：2026年10月10日，下午4:20-5:20

报告地点：览秀楼105学术报告厅

报告摘要：Portfolio theory prescribes that a mean-variance investor should always combine the risk-free asset with the tangency portfolio of risky assets. We show that this prescription can fail once the optimal portfolio must be estimated from data. This is surprising because the risk-free asset is the one asset whose return is free of estimation risk, and because the literature on estimation risk recommends shifting wealth from the risky assets to the risk-free asset. The reason is that adding the risk-free asset introduces an additional weight to estimate, and this weight determines the investor's aggregate exposure to the risky assets. The estimation error in that weight can outweigh the benefit of the risk-free asset. We derive the expected out-of-sample utility of the estimated optimal portfolios with and without the risk-free asset. Discarding the risk-free asset is preferable if and only if the investor's risk aversion lies in an interval around the risk-aversion coefficient for which the investor considers a portfolio without the risk-free asset to be optimal, and we characterize this interval. The interval is wide for realistic parameter values, and the conclusion survives even when the investor uses two-fund rules that mitigate estimation risk.

报告人简介：Nathan Lassance is an Associate Professor of finance at UCLouvain in Belgium. He works on parameter uncertainty and its consequences for portfolio selection and asset pricing. His research has been published in Management Science, Operations Research, the Journal of Financial and Quantitative Analysis, and the Journal of Business & Economic Statistics.