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Stocks are exposed to the risk of sudden downward jumps. Additionally, a crash in one stock (or index) can increase the risk of crashes in other stocks (or indices). Our paper explicitly takes this contagion risk into account and studies its impact on the portfolio decision of a CRRA investor both in complete and in incomplete market settings. We find that the investor significantly adjusts his portfolio when contagion is more likely to occur. Capturing the time dimension of contagion, i.e. the time span between jumps in two stocks or stock indices, is thus of first-order importance when analyzing portfolio decisions. Investors ignoring contagion completely or accounting for contagion while ignoring its time dimension suffer large and economically significant utility losses. These losses are larger in complete than in incomplete markets, and the investor might be better off if he does not trade derivatives. Furthermore, we emphasize that the risk of contagion has a crucial impact on investors' security demands, since it reduces their ability to diversify their portfolios.
Real options theory applies techniques known from finance theory to the valuation of capital investments. The present paper investigates further into this analogy, considering the case of a portfolio of real options. An implementation of real option models in practice will mostly be concerned with a portfolio of real options, so the analysis of portfolio aspects is of both academic and practical interest. Is a portfolio of real options special? In order to shed some light on this question, the present paper will outline the relevant features of a portfolio of real options. It will show that the analogy to financial options remains great if compound option models are applied. As a result, a portfolio of real options, and therefore the firm as such, generally is to be understood as one single compound, real option.
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.
An economy in which deposit-taking banks of a Diamond/ Dybvig style and an asset market coexist is modelled. Firstly, within this framework we characterize distinct financial systems depending on the fraction of households with direct investment opportunities that are less efficient than those available to banks. With this fraction comparatively low, the evolving financial system can be interpreted as market-oriented. In this system, banks only provide efficient investment opportunities to households with inferior investment alternatives. Banks are not active in the secondary financial market nor do they provide any liquidity insurance to their depositors. Households participate to a large extent in the primary as well as in the secondary financial markets. In the other case of a relatively high fraction of households with inefficient direct investment opportunities, a bank-dominated financial system arises, in which banks provide liquidity transformation, are active in secondary financial markets and are the only player in primary markets, while households only participate in secondary financial markets. Secondly, we analyze the effect a run on a single bank has on the entire financial system. Interestingly, we can show that a bank run on a single bank causes contagion via the financial market neither in market-oriented nor in extremely bank-dominated financial systems. But in only moderately bank-dominated (or hybrid) financial systems fire sales of long-term financial claims by a distressed bank cause a sudden drop in asset prices that precipitates other banks into crisis.
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
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
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.
We compute the optimal dynamic asset allocation policy for a retiree with Epstein-Zin utility. The retiree can decide how much he consumes and how much he invests in stocks, bonds, and annuities. Pricing the annuities we account for asymmetric mortality beliefs and administration expenses. We show that the retiree does not purchase annuities only once but rather several times during retirement (gradual annuitization). We analyze the case in which the retiree is restricted to buy annuities only once and has to perform a (complete or partial) switching strategy. This restriction reduces both the utility and the demand for annuities.
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.