Table of Contents
Economics Research International
Volume 2010, Article ID 340181, 10 pages
Research Article

Estimation Risk Modeling in Optimal Portfolio Selection: An Empirical Study from Emerging Markets

1Business Division, Mahidol University International College, Nakhonpathom 73170, Thailand
2Thammasat Business School, Thammasat University, Bangkok 10200, Thailand

Received 3 March 2010; Accepted 22 June 2010

Academic Editor: Benjamin Miranda Tabak

Copyright © 2010 Sarayut Nathaphan and Pornchai Chunhachinda. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Efficient portfolio is a portfolio that yields maximum expected return given a level of risk or has a minimum level of risk given a level of expected return. However, the optimal portfolios do not seem to be as efficient as intended. Especially during financial crisis period, optimal portfolio is not an optimal investment as it does not yield maximum return given a specific level of risk, and vice versa. One possible explanation for an unimpressive performance of the seemingly efficient portfolio is incorrectness in parameter estimates called “estimation risk in parameter estimates”. Six different estimating strategies are employed to explore ex-post-portfolio performance when estimation risk is incorporated. These strategies are traditional Mean-Variance (EV), Adjusted Beta (AB) approach, Resampled Efficient Frontier (REF), Capital Asset Pricing Model (CAPM), Single Index Model (SIM), and Single Index Model incorporating shrinkage Bayesian factor namely, Bayesian Single Index Model (BSIM). Among the six alternative strategies, shrinkage estimators incorporating the single index model outperform other traditional portfolio selection strategies. Allowing for asset mispricing and applying Bayesian shrinkage adjusted factor to each asset's alpha, a single factor namely, excess market return is adequate in alleviating estimation uncertainty.