This paper examines whether investors receive compensation for holding stocks with a strong sensitivity to extreme market downturns in a sample covering forty countries. Worldwide, stocks with strong crash sensitivity deliver average returns of more than 7% p.a. higher than stocks with weak crash sensitivity. The effect is robust across geographical subsamples and is not explained by systematic risk factors and alternative firm characteristics. I show that the risk premium is particularly pronounced in countries that display negative market skewness, high income per capita, and rank high on Hofstede's individualism index.
We investigate the optimization of a securitized asset pool for collateralized loan obligation (CLO) transactions. Defining economic risk transfer as the objective function for optimization and the crucial underlying motivation for banks to engage in balance-sheet CLOs, we present the mathematical description of this optimization problem. The criteria applied by rating agencies to CLO transactions add both linear and non-linear constraints to this optimization task. As such problems may not be solved in closed form, we develop a heuristic algorithm and test it with realistic credit portfolio data. The application of this algorithm could support banks as originators when selecting an optimal securitized portfolio from an eligible asset pool. The algorithm becomes particularly relevant when considering the current credit crisis and the subsequent need for liquidity in banking: retained CLO tranches could be used as collateral in repo transactions with central banks and establish an alternative source of funding.
Model builders face ambiguity about the true data generating process. Consequently, they need to deal with ambiguity attitudes (inside uncertainty) and ambiguous financial reality (outside uncertainty) when developing and estimating financial models. We introduce a novel approach for systematically dealing with outside uncertainty in addition to inside uncertainty in a tractable way. By bounding the effects of ambiguous data features, we avoid the adverse consequences of outside uncertainty, such as strongly biased equity premiums and investment policies. In a real data application, we show that asset managers can be more reliably evaluated using our bounded-influence approach.
A typical problem arising in financial planning for private investors consists in the fact that the initial investor's portfolio, the one determined by the consulting process of the financial institution and the universe of instruments made available to the investor have to be matched/optimised when determining the relevant portfolio choice. We call this problem the three-portfolios matching problem. Clearly, the resulting portfolio selection should be as close as possible to the optimal asset allocation determined by the consulting process of the financial institution. However, the transition from the investor's initial portfolio to the final one is complicated by the presence of transaction costs and some further more specific constraints. Indeed, usually the portfolios under consideration are structured at different aggregation levels, making portfolios comparison and matching more difficult. Further, several investment restrictions have to be satisfied by the final portfolio choice. Finally, the arising portfolio selection process should be sufficiently transparent in order to incorporate the subjective investor's trade-off between the objectives ‘optimal portfolio matching' and ‘minimal portfolio transition costs'. In this paper, we solve the three-portfolios matching problem analytically for a simplified setting that illustrates the main features of the arising solutions and numerically for the more general situation.
