Do investors gain from forecasting the asymmetric return comovements of financial and real assets?

Recent research on asset allocation emphasizes the importance of considering non-traditional asset classes such as commodities and real estate—the former for their di-versification properties, and the latter due to its importance in the average investor’s portfolio. However, modelling and forecasting asset return comovements is challenging because the dependence structure is dynamic, regime-specific, and non-elliptical. Moreover, little is known about the economic source of this time-varying dependence or how to use this information to improve investor portfolios. We use a flexible framework to assess the economic value to investors of incorporating better forecast-ing information about return comovements between equities, bonds, commodities, and real estate. The dependence structure is allowed to be dynamic and non-elliptical, while the state variables follow Markov-switching stochastic volatility processes. We find that the predictability of return comovements is significantly improved by incor-porating macro and non-macroeconomic variables, in particular inflation uncertainty and bond illiquidity. The economic value added to investors is significant across levels of risk aversion, and the model outperforms traditional multivariate GARCH frameworks.