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

Research output: Contribution to journalArticlepeer-review



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.


Original languageEnglish
Pages (from-to)1-46
Number of pages46
JournalInternational Journal of Finance and Economics
Early online date10 Aug 2020
Publication statusE-pub ahead of print - 10 Aug 2020


  • Comovement, Asset allocation, Portfolio optimisation, Copula, Markov models, Macroeconomic variables, Regimes, Economic value added, Volatility