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Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.
JEL classification: G2; G28; C52
Structural positions are very common in investment practice. A structural position is defined as a permanent overweighting of a riskier asset class relative to a prespecified benchmark portfolio. The most prominent example for a structural position is the equity bias in a balanced fund that arises by consistently overweighting equities in tactical asset allocation. Another example is the permanent allocation of credit in a fixed income portfolio with a government benchmark. The analysis provided in this article shows that whenever possible, structural positions should be avoided. Graphical illustrations based on Pythagorean theorem are used to make a connection between the active risk/return and the total risk/return framework. Structural positions alter the risk profile of the portfolio substantially, and the appeal of active management – to provide active returns uncorrelated to benchmark returns and hence to shift the efficient frontier outwards – gets lost. The article demonstrates that the commonly used alpha – tracking error criterion is not sufficient for active management. In addition, structural positions complicate measuring managers’ skill. The paper also develops normative implications for active portfolio management. Tactical asset allocation should be based on the comparison of expected excess returns of an asset class to the equilibrium risk premium of the same asset class and not to expected excess returns of other asset classes. For the cases, where structural positions cannot be avoided, a risk budgeting approach is introduced and applied to determine the optimal position size. Finally, investors are advised not to base performance evaluation only on simple manager rankings because this encourages managers to take structural positions and does not reward efforts to produce alpha. The same holds true for comparing managers’ information ratios. Information ratios, in investment practice defined as the ratio of active return to active risk, do not uncover structural positions.