Pension plans in Switzerland favor active management over indexing to implement their strategic asset allocation. Empirical surveys show, however, that their success has been below expectations, as the median performance of Swiss pension plans in domestic and international equities is below market indices even gross of fees. The results of this paper's survey across decisionmakers of Swiss pension plans sheds some light on why active management is still so popular across Swiss pension plans. On average the participants in the sample are prone to the better-than-average-effect. A majority expects their managers and their overall pension plan to outperform the other survey participants in the future. The subjective perceptions of the own skill level relative to the competitors can explain the popularity of active management across Swiss pension plans.
In this paper I employ a dynamic general equilibrium model to study macroe-conomic e®ects and welfare implications of alternative reforms to the U.S. health insurance system. In particular, I focus on expanding Medicare to the entire population, extending Medicaid, and having an individual mandate as well as other related medical reforms. All these reforms can be ¯nanced in several ways. I consider a stochastic OLG framework with heterogeneous agents facing uncertain health shocks. Individuals make optimal decisions on labor supply, health insurance, and medical services. As the amount of optimal medical consumption and hours worked are endogenous, this environment captures general equilibrium
effects. The model is calibrated to the U.S. data. Numerical simulations indicate that reforming the health insurance system has several important macroeconomic
effects on health expenditures, hours worked, and welfare.
Parametric option pricing models are widely used in finance. These models capture several features of asset price dynamics; however, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this article we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density, because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method, called automatic correction of errors (ACE), estimates the state price distribution nonparametrically. The method is easy to implement and can be combined with any model-based pricing formula to correct the systematic biases of pricing errors. We also develop a nonparametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on S&P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.
We use experimental stock markets to add more evidence that Black's [1976. Proceedings of the 1976 Meeting of the Business and Economic Statistics Section. American Statistical Association, pp. 177–181] leverage effect in financial markets does not necessarily stem from the financial leverage of the firm. We surprisingly find a large number of markets in which the leverage effect is observed although the underlying asset does not exhibit any financial leverage at all.
Für die Zürcher Banking-Professorin Christine Hirszowicz sind die Attacken der USA und der EU gegen Steueroasen reine Symptombekämpfung. Ursache der Kapitalflucht seien zu hohe Steuertarife in den Ursprungsländern. Der Schweiz rät sie, auch bei Steuerhinterziehung Rechtshilfe zu leisten, sonst aber hart zu bleiben.
Two computable expressions for the exact density of a ratio of quadratic forms in Gaussian random vectors are derived, one of which is restricted to special cases of the problem. Ratios of this type are ubiquitous in econometrics, but their density, unlike the corresponding cumulative distribution function, has not received much attention to date. The new algorithms complement those available for the latter. The included performance study demonstrates the accuracy of the two algorithms, both absolute and relative to each other, and allows general recommendations on their use to be made.
Closed-form approximations for the density and cumulative distribution function of the doubly noncentral t distribution are developed based on saddlepoint methods. They exhibit remarkable accuracy throughout the entire support of the distribution and are vastly superior to existing approximations. An application in finance is considered which capitalizes on the enormous increase in computational speed.
A new point estimator for the AR(1) coefficient in the linear regression model with arbitrary exogenous regressors and stationary AR(1) disturbances is developed. Its construction parallels that of the median--unbiased estimator, but uses the mode as a measure of central tendency. The mean--adjusted estimator is also considered, and saddlepoint approximations are used to lower the computational burden of all the estimators. Large--scale simulation studies for assessing their small--sample properties are conducted. Their relative performance depends almost exclusively on the value of the autoregressive parameter, with the new estimator dominating over a large part of the parameter space.
