Portfolio Optimization Using Time Series Models for Predicting the Distribution of Returns

Abstract

In recent years, portfolio optimization with the use of a vector autoregressive (VAR) model to estimate future stock returns has been subject of research. In this paper, we propose an optimization model that uses time series models to predict not only the future returns but also the conditional variance-covariance matrix of returns. More speci?cally, the future returns are predicted by using the VAR model, and the conditional variance-covariance matrix is estimated by using a dynamic conditional correlation (DCC) multivariate GARCH model. We evaluate the out-of-sample investment performance of our model using historical data of U.S. stock market.