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Performance fees for portfolio managers are designed to align the managers' goals with those of the investors and to motivate managers to aquire "superior" information and to make better investment decisions. A part of the literature analyzes performance fees on the basis of market valuation. In this article it is shown that market valuation faces a dilemma: on the one hand, the conditions which allow for market valuation imply that the portfolio manager perfectly hedges the performance fee. This in turn implies severe restrictions on the incentive effects of the performance fee. In particular, the fee does not motivate the manager to use superior information for investment decisions concerning the managed portfolio. On the other hand, better incentives can only be generated under conditions which exclude market valuation. In this case, the analysis has to be based on expected utility valuation. Keywords: performance fees, portfolio managers, "superior" information, market valuation, incentive effects
Rating agencies state that they take a rating action only when it is unlikely to be reversed shortly afterwards. Based on a formal representation of the rating process, I show that such a policy provides a good explanation for the puzzling empirical evidence: Rating changes occur relatively seldom, exhibit serial dependence, and lag changes in the issuers’ default risk. In terms of informational losses, avoiding rating reversals can be more harmful than monitoring credit quality only twice per year.
The paper documents lack of awareness of financial assets in the 1995 and 1998 Bank of Italy Surveys of Household Income and Wealth. It then explores the determinants of awareness, and finds that the probability that survey respondents are aware of stocks, mutual funds and investment accounts is positively correlated with education, household resources, long-term bank relations and proxies for social interaction. Lack of financial awareness has important implications for understanding the stockholding puzzle and for estimating stock market participation costs. Klassifikation: E2, D8, G1
In recent years new methods and models have been developed to quantify credit risk on a portfolio basis. CreditMetrics (tm), CreditRisk+, CreditPortfolio (tm) are among the best known and many others are similar to them. At first glance they are quite different in their approaches and methodologies. A comparison of these models especially with regard to their applicability on typical middle market loan portfolios is in the focus of this study. The analysis shows that differences in the results of an application of the models on a certain loan portfolio is mainly due to different approaches in approximating default correlations. That is especially true for typically non-rated medium-sized counterparties. On the other hand distributional assumptions or different solution techniques in the models are more or less compatible.
Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
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
The classical approaches to asset allocation give very different conclusions about how much foreign stocks a US investor should hold. US investors should either allocate a large portion of about 40% to foreign stocks (which is the result of mean/variance optimization and the international CAPM) or they should hold no foreign stocks at all (which is the conclusion of the domestic CAPM and mean/variance spanning tests). There is no way in between.
The idea of the Bayesian approach discussed in this article is to shrink the mean/variance efficient portfolio towards the market portfolio. The shrinkage effect is determined by the investor's prior belief in the efficiency of the market portfolio and by the degree of violation of the CAPM in the sample. Interestingly, this Bayesian approach leads to the same implications for asset allocation as the mean-variance/tracking error criterion. In both cases, the optimal portfolio is a combination of the market portfolio and the mean/variance efficient portfolio with the highest Sharpe ratio.
Applying both approaches to the subject of international diversification, we find that a substantial home bias is only justified when a US investor has a strong belief in the global mean/variance efficiency of the US market portfolio and when he has a high regret aversion of falling behind the US market portfolio. We also find that the current level of home bias can be justified whenever-regret aversion is significantly higher than risk aversion.
Finally, we compare the Bayesian approach of shrinking the mean/variance efficient portfolio towards the market portfolio to another Bayesian approach which shrinks the mean/variance efficient portfolio towards the minimum-variance portfolio. An empirical out-of-sample study shows that both Bayesian approaches lead to a clearly superior performance compared to the classical mean/variance efficient portfolio.
The purpose of this paper is to compare three different index construction methodologies of commercial property investments. We examine for different European countries (i) appraisal-based indices and methods of „unsmoothing“ the corresponding return series, (ii) indices that trace average ex-post transaction prices over time, and (iii) indices based on Real Estate Investment Trust share prices.
Real estate is an important asset, but as a direct investment subject to several difficulties. Shares of public open end funds or of real estate stock corporations represent a possible way for an investor to avoid these problems. The focus of this paper is the analysis of inflation risk of European real estate securities. An overview of the institutional frameworks regarding these companies is given. The returns of real estate securities in France, Germany, Switzerland and the United Kingdom are examined for the period 1980:1-1998:12. Besides the classical Fama/Schwert-approach, shortfall risk measurements have been used. In this context, transaction costs in particular have been taken into account.
In this paper we have developed a financial model of the non-life insurer to provide assistance for the management of the insurance company in making decisions on product, investment and reinsurance mix. The model is based on portfolio theory and recognizes the stochastic nature of and the interaction between the underwriting and investment income of the insurance business. In the context of an empirical application we illustrate howa portfolio optimisation approach can be used for asset-liability management.