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Bessler, W., Opfer, H. and Wolff, D. (2017) Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black–Litterman, Mean-Variance, and Naïve Diversification Approaches. The European Journal of Finance, 23, 1-30.
https://doi.org/10.1080/1351847X.2014.953699

has been cited by the following article:

• TITLE: A Method for Portfolio Selection Based on Joint Probability of Co-Movement of Multi-Assets

  AUTHORS: Tianmin Zhou

  KEYWORDS: Portfolio Selection, Mean-Variance, Joint Probability Density, Cross-Entropy Approach

  JOURNAL NAME: Journal of Mathematical Finance, Vol.8 No.3, August 7, 2018

  ABSTRACT: This paper presents a method of portfolio selection for reducing co-related risks. Differing from the Markowitz’s mean-variance framework, we use the joint probability of co-movement of multi-assets (JPCM) as a measure of risks, and under the condition of minimizing the JPCM, we pinpoint the optimal portfolio by optimizing the JPCM matrix of paired assets. At the same time, we use the shape parameter of generalized error distribution (GED) to measure the tail shapes of different portfolios. The empirical results for China’s stock market show that the JPCM portfolios significantly outperform naive-diversified portfolios (1/N-rule) and minimum-variance (MV) in terms of the tail shape of portfolio distribution.