Out-of-sample diversified portfolio performance with Minimum-Variance, Inverse-Variance, and Hierarchical Risk Parity methods

This research aims to find a portfolio optimization method that has superior performance in outof-sample simulation in the Indonesia equitymarket. The portfolio will be optimized utilizing three methods: Inverse-Variance, Minimum-Variance, and Hierarchical Risk Parity.Portfolio performance, i.e. Sharpe Ratio, iscalculated using the actual return generated by the portfolio. The accuracy of the portfolio’s model is measured using the sum of squared errors and thet-test between the actual return and expected return of the portfolio in out-of-sample simulations. The out-of-sample simulations will be carried out in 3 scenarios of holding period and rebalancing at the end of the holding period.The simulation results suggest that the Hierarchical Risk Parity method outperformed the other two portfolio optimization methods, having the highest actual return, Sharpe ratio, and accuracy in predicting returns. This research concludes that a more sophisticated approach is required to build an equity portfolio with better performance in Indonesia.