Dynamic Autocorrelation and International Portfolio Allocation

We explore the relevance of dynamic autocorrelation in modeling expected returns and allocating funds between developed and emerging stock markets. Using stock market data for the US and Latin America, we find that autocorrelation in monthly returns vary with conditional volatility, implying some investors implement feedback trading strategies. Dynamic autocorrelation models fit the data considerably better than a conditional version of the zero-beta CAPM, while differences between models with an autoregressive term are modest. Investors can improve their portfolio optimization between developed and emerging stock markets by considering time-varying autocorrelation. The most drastic difference in portfolio performance is not due to allowing autocorrelation to vary over time, but realizing that stock returns are autocorrelated, especially in emerging stock markets.