This paper studies the optimal dynamic asset allocation between bonds, stocks, and cash of an ambiguity averse investor. While ambiguity aversion possibly leads to non-participation in the stock market, the investor always invests in bonds to hedge interest rate risk, showing that non-participation results prevail for the stock market but do not apply to the bond market. The bond-stock ratio increases in the investor's risk aversion whenever it is well-defined, providing a robust explanation for the asset allocation puzzle. An application to the US market yields an investment in stocks for speculative reasons and an investment in bonds solely for hedging purposes with a bond-stock ratio increasing in the investor's risk aversion. Quantitatively, ambiguity aversion leads to less leveraged portfolios and a shift to a large fraction of wealth held in cash towards the investor's time horizon.
We study how the level of systemic risk empirically observed in an interbank network differs from the expected risk implied by the balance sheets composition of the participating banks, abstracting from the specific configuration of bilateral exposures. To this end, using different contagion models (i.e. interbank asset valuation functions), we measure observed systemic risk on e-MID network data from 2005 to 2012 and compare it with the expected risk generated by a null model: an ensemble of interbank networks whose links represent possible market clearing configurations. This counterfactual is obtained through a maximum-entropy approach that preserves banks' balance sheet variables and diversification patterns, while randomizing the configuration of exposures. We find that the aggregate levels of observed and expected systemic risks are generally consistent but diverge significantly during turbulent periods-specifically following the default of Lehman Brothers (2009) and the VLTRO implementation by the ECB (2012). At the individual level, however, banks often appear more or less risky than implied by their balance sheet alone, due to their position in the network. Our results show, on the one hand, that balance sheet information embedded in a suitable maximum-entropy framework provides reliable estimates of aggregate systemic risk; on the other hand, they highlight the importance of detailed network information for accurate stress testing of individual banks, particularly after systemic events.
