A report from the portfolio committee on welfare / UNICEF workshop on children and development

Honourable Cassiem Saloojee, MP chaired the workshop. He noted that the workshop was intended to provide an opportunity to assess several recent research studies related to children and development in South Africa. People who had been invited included MPs, members of NGOs and CBOs and government officials. A list of participants is attached

Complex Valued Risk Diversification

Risk diversification is one of the dominant concerns for portfolio managers. Various portfolio constructions have been proposed to minimize the risk of the portfolio under some constrains including expected returns. We propose a portfolio construction method that incorporates the complex valued principal component analysis into the risk diversification portfolio construction. The proposed method is verified to outperform the conventional risk parity and risk diversification portfolio constructions

Risk minimization and portfolio diversification

We consider the problem of minimizing capital at risk in the Black-Scholes setting. The portfolio problem is studied given the possibility that a correlation constraint between the portfolio and a financial index is imposed. The optimal portfolio is obtained in closed form. The effects of the correlation constraint are explored; it turns out that this portfolio constraint leads to a more diversified portfolio

Extend the ideas of Kan and Zhou paper on Optimal Portfolio Construction under parameter uncertainty

In this dissertation, we extend the ideas of Raymond Kan and Guofu Zhou for optimal portfolio construction under parameter uncertainty. Kan and Zhou proved analytically that under parameter uncertainty, investing in the sample tangency portfolio and the riskless is not optimal. Based on this idea we will approach the portfolio construction under parameter uncertainty in a different way. We will optimise the expected out-of-sample performance of a portfolio using a numerical approach. Using Monte Carlo simulations we will develop an algorithm that calculates the expected out-of-sample performance of any portfolio rule. We will then extend this algorithm in order to be able to input new portfolio rules and test their performance.\ud \ud The new portfolio rules we introduce are based on shrinkages for the mean and covariance matrix of the assets returns. These shrinkages will have some parameters that will be chosen so that we optimise the expected out-of-sample performance of the input portfolio rule. A comparison is then done between the portfolio rules we introduce and Kan and Zhou portfolio rules