演講者：李華教授

單　位：長春大學統計系

日　期：2014年6月3日（星期二）　PM 2:30

地　點：國立高雄大學理學院450室

講　題：Efficient Estimation for Markowitz Portfolio Optimization by Using Random Matrix Theory

摘　要：

The Markowitz mean-variance optimization procedure is one of the most highly appreciated theories in literature. Given a set of assets, it enables investors to find the best allocation of wealth incorporating their preferences as well as their expectations of returns and risks. It is expected to be a powerful tool for investors to allocate their wealth efficiently.

However, it has been demonstrated to be less applicable in practice. The portfolio formed by using the classical Mean-Variance approach always results in extreme portfolio weights that fluctuate substantially over time and perform poorly in the sample estimation as well as in the out-of-sample forecasting. The reason for this problem is due to the substantial estimation error of the inputs of the optimization procedure. The classical mean-variance approach which uses the sample mean and sample covariance matrix as inputs always results in serious departure of its estimated optimal portfolio allocation from its theoretical counterpart.

In this paper, we provide an eigenvalue-corrected estimation which performs better than the plug-in estimation and the bootstrap-corrected estimation not only in the return but also in the risk. At same time, we deduce the limiting behaviors of  According the simulation result, we find the limiting results very close to the real results. So we suggest company consider the limiting express as their estimation.