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T. Dangl, A. Weissensteiner:
"Long-term asset allocation under time-varying investment opportunities: Optimal portfolios with parameter and model uncertainty";
Vortrag: Seminar am Stevens Institute of Technology, NJ, USA, Hoboken, New Jersey, USA (eingeladen); 24.04.2018.

We study the implications of predictability on the optimal asset allocation of ambiguity averse long-term investors. We analyze the term structure of the multivariate risk-return trade-off in a VAR model under full consideration of parameter uncertainty, and we decompose the predictive covariance along different sources of risk/uncertainty. We calibrate the model to real returns of US stocks, US long-term government bonds, cash, real-estate and gold using the term spread and the dividend-price ratio as additional predictive variables. While over short periods the model-implied conditional covariance structure of asset-class returns determines the optimal allocation, we find that over longer horizons the optimal asset allocation is significantly influenced by the covariance structure induced by estimation errors. As a consequence, the ambiguity averse long-term investor tilts her portfolio not simply toward the global minimum-variance portfolio but shrinks portfolio weights toward a seemingly inefficient portfolio which shows maximum robustness against estimation errors. Most interestingly, we find that even though time diversification of stock returns vanishes after consideration of estimation errors, real long-term bond returns are even more affected, making stocks an important asset class for the ambiguity averse long-term investor.

Portfolio Choice, Predictability, Parameter Uncertainty, Ambiguity Aversion, Strategic Asset Allocation