Asset allocation with recursive parameter updating and macroeconomic regime identifiers

This article studies long-horizon dynamic asset allocation strategies with recursive parameter updating. The parameter estimates for the regime-switching dynamics vary as more and more datapoints are observed and the sample size increases. In such a setting, the globally optimal portfolio strategy cannot be determined due to computational complexity. Among a set of suboptimal strategies, the portfolio performance can be improved substantially if the dynamics of the regimes are estimated from fundamental macroeconomic instead of financial return data. Especially after highly uncertain times, the estimation based on financial market data identifies extreme regimes, leading to extreme hedging demands against regime changes.