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Do investors gain from forecasting the asymmetric return co‐movements of financial and real assets?
Author(s) -
Mandal Anandadeep,
Poshakwale Sunil S.,
Power Gabriel J.
Publication year - 2021
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.1961
Subject(s) - economics , real estate , diversification (marketing strategy) , bond , portfolio , volatility (finance) , econometrics , financial economics , asset allocation , risk premium , predictability , finance , business , physics , marketing , quantum mechanics
Recent research on asset allocation emphasizes the importance of considering non‐traditional asset classes such as commodities and real estate—the former for their diversification properties, and the latter due to its importance in the average investor's portfolio. However, modelling and forecasting asset return co‐movements 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 forecasting information about return co‐movements 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 co‐movements is significantly improved by incorporating 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.