An Empirical Assessment of Low Volatilty Portfolio Construction Techniques in the South African Environment

Abstract

Portfolios constructed to have low volatility characteristics have received increasing interest in recent years. This is due to the fact that these portfolios returning higher risk-adjusted returns than market-capitalization weighted portfolios in both international markets and the South African domestic market. The outperformance of the portfolios are particularly fascinating given that economic theory suggests that higher risk is typically expected to be compensated by higher expected return. In this study, we analyze the performance of low volatility portfolios using a variety of construction techniques in South African markets using the stocks listed on the JSE. Our results compare the performance of the different techniques and show substantial outperformance of these portfolios in the South African environment relative to their market capitalization-weighted equity benchmark counterpart (ALSI). In addition, the low volatility portfolios are also blended with typical portfolios (SWIX index) in order to establish their effectiveness as useful portfolio strategies.

Notes

1 The Capital Asset Pricing Model helps investors determine their return by using a formula that explains the relationship between expected return and risk: Expected Rate of Return = r = rf + B (rm - rf). where: rf is the risk-free rate an investor would receive from a risk-free investment. B is the beta of a stock (measuring the risk of the security) and rm is the expected return of the market.

2 We will use variance as a measure of risk in this article.

3 They used both asymptotic principal component methods (Connor & Korajczyk, 1988) and Bayesian shrinkage methods (Ledoit & Wolf, 2003).

4 The Gini index is a measure of dispersion using the Lorenz curve (Maillard et al., 2008). The Lorenz curve is a graphical representation of the cumulative distribution of the distribution of wealth in a society, where the statistics of interest may be the income of a population. Mathematically, the Gini index G is computed as: , where, L(x) is the Lorenz curve. By applying this concept to the low volatility portfolios, the statistics of interest become the weights and risk contributions of a portfolio.