Asymptotic Behavior of the Alpha-Risk Minimizing Portfolio in High-Dimensional Setting

• Activity Number: 34 - Linear Models for Large or Complex Data
• Type: Contributed
• Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Hiroyuki Taniai*
• Companies: Waseda University
• Keywords: quantile regression; high-dimensional data; empirical process; semiparametrics

Abstract

In this talk, we extend the minimization problem of some quantile-restricted models to the "large p, small n" paradigm. Namely, letting the observation period increase slowly compared to the huge number of elements, we try to find an optimal solution which satisfies some quantile-based constraints. For this purpose, we will modify the results on the simultaneous consistency of the marginal distributions, to apply it to the regression rankscores, the dual of the regression quantiles. We also plan to consider its efficient construction as well.