Enhanced minimum variance optimisation: a pragmatic approach

• Main Author: Lakhoo, Lala Bernisha Janti
• Other Authors: Bradfield, David
• Format: Dissertation
• Language: English
• Published: University of Cape Town 2017
• Subjects: Statistical Sciences; Advanced Analytics And Decision Sciences
• Online Access: http://hdl.handle.net/11427/23764

Since the establishment of Markowitz's theory, numerous studies have been carried out over the past six decades or so that cover the benefits, limitations, modifications and enhancements of Mean Variance (MV) optimisation. This study endeavours to extend on this, by means of adding factors to the minimum variance framework, which would increase the likelihood of outperforming both the market and the minimum variance portfolio (MVP). An analysis of the impact of these factor tilts on the MVP is carried out in the South African environment, represented by the FTSE-JSE Shareholder weighted Index as the benchmark portfolio. The main objective is to examine if the systematic and robust methods employed, which involve the incorporation of factor tilts into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured covariance target and shrinkage estimator. The results of this study showed that with specific levels of factor tilting, one can generally improve both absolute and risk-adjusted performance and lower concentration levels in comparison to both the MVP and benchmark. Additionally, lower turnover levels were observed across all tilted portfolios, relative to the MVP. Furthermore, covariance shrinkage enhanced all portfolio statistics examined, but significant improvement was noted on drawdown levels, capture ratios and risk. This is in contrast to the results obtained when the standard sample covariance matrix was employed.