Initial public offerings: an asset allocation decision based on nonnormal returns

Abstract

This study analyses whether the inclusion of initial public offering (IPO) stocks as part of an optimal asset allocation strategy can reduce the systematic risk of an investment portfolio. The asset allocation framework takes account of nonnormality of returns from a universe of eight asset classes using 240 monthly returns between January 1992 and December 2011. AR-GJR-GARCH models resolve the tail dependence and heteroscedasticity in the return series. Generalized Pareto distributions help to fit the heavy-tailed return distributions, while copula functions help to calibrate the dependence structure between the asset returns. The optimization algorithm persistently includes IPO stocks as part of an optimal asset allocation strategy. Their portfolio inclusion reduces the conditional value-at-risk and improves the risk-return trade-off.