A simulation analysis of returns-risk portfolio optimization models

Portfolio optimization is the process of choosing the best investment decision across a set of financial instruments or assets. Investors seek to maximize their (expected) returns, but higher expected return usually means taking on more risk. So, investors are faced with a tradeoff between risk and expected return. This problem can be approached in several ways, including: maximize a portfolio’s expected return for a given risk, minimize a portfolio’s risk for a given expected return, or apply a fractional model with a returns over risk ratio that keeps both expected returns and risk flexible simultaneously. The objective of this paper is to compare these modeling strategies through real world financial data as well as simulation of returns using the Moving Average-Integrated Generalized Autoregressive Conditional Heteroskedastic (MA-IGARCH) model. Our results find that the ratio model provides a more balanced portfolio and provides more stable results than the other two approaches.