Robust analysis for downside risk in portfolio management for a volatile stock market

Variance and downside risk are different proxies of risk in portfolio management. This study tests mean-variance and downside risk frameworks in relation to portfolio management. The sample is a highly volatile market; Karachi Stock Exchange, Pakistan. Factors affecting portfolio optimization like appropriate portfolio size, portfolio sorting procedure, butterfly effect on the choice of appropriate algorithms and endogeneity problem are discussed and solutions to them are incorporated to make the study robust. Results show that downside risk framework performs better than Markowitz mean-variance framework. Moreover, this difference is significant when the asset returns are more skewed. Results suggest the use of downside risk in place of variance as a measure of risk for investment decisions.