Publications

In this paper we examine a model where firms decide on the intensity of information acquisition about shocks. We analyze how the monetary policy framework impacts on the aggregate amount of information collected by firms. We show that it is socially beneficial to delegate monetary policy to a conservative central bank even if there are no incentives to push output above its long-run level. Transparency of central banks about economic shocks has ambiguous effects on welfare. If an extreme level of opacity is feasible, it represents the social optimum. Otherwise full transparency may be a second-best solution.

We examine the effects of non-portfolio risks on optimal portfolio choice. Examples of non-portfolio risks include, among others, uncertain labor income, uncertainty about the terminal value of fixed assets such as housing and uncertainty about future tax liabilities. In particular, while some of these risks are added to portfolio value and have been amply studied, others are multiplicative in nature and have received far less attention. Moreover, the combined effects of multiple risks lead to some seemingly paradoxical choice behavior. We rationalize such behavior and we show how non-portfolio risks might lead to seemingly U-shaped relative risk aversion for a representative investor, as found empirically by Ait-Sahilia and Lo (2000) and Jackwerth (2000).

This paper discusses the incentive effect of tracking policies in education. The results contradict the argument by Hanushek and Woessmann (2006), that differences-in-differences estimations capture the full impact of tracking. Such estimations capture the gain in education between the measurements but neglect the gains before the first measurement. In a standard incentive theory framework these gains and their variance differ systematically between tracking and comprehensive systems. In a selective educational system, most students will provide more effort in early education. Signaling considerations enhance this line of argument. As a result, educational tests alone do not reveal the efficiency and distributional aspects of educational policies sufficiently.

A recent trend in the German Asset Backed Securities (ABS) market is the securitisation of subordinated loans and profit participation agreements (PPAs) granted to medium-sized enterprises (MEs). This paper provides an overview of this growing market and analyses the benefits of such transactions for the portfolio companies as well as originators and potential investors. Simulations of ten recent transactions indicate that despite of relatively low interest rates charged on obligors, originators and investors can earn attractive returns at fairly low risk. In particular, the junior tranches of these securitisations exhibit quite attractive risk-return profiles.

This paper surveys the empirical evidence on causal effects of education on earnings for Germany and compares alternative studies in the light of their underlying identifying assumptions. We work out the different assumptions taken by various studies, which lead to rather different interpretations of the estimated causal effect. In particular, we are interested in the question to what extend causal return estimates are informative regarding educational policy advice.
Despite the substantial methodological differences, we have to conclude that the empirical findings for Germany are quite robust and do not deviate substantially from each other. This also holds for the few studies which rely on ignorability conditions, regardless of whether they use educational attainment as a continuous treatment variable or as a discrete treatment indicator. Own estimates based on the matching approach indicate that the selection into upper secondary schooling is suboptimal.

Modelling and forecasting the covariance of financial return series has always been a challenge due to the so-called "curse of dimensionality". This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models.

We develop a panel intensity model, with a time varying latent factor, which captures the influence of unobserved time effects and allows for correlation across individuals. The model is designed to analyze individual trading behavior on the basis of trading activity datasets, which are characterized by four dimensions: an irregularly-spaced time scale, trading activity types, trading instruments and investors. Our approach extends the stochastic conditional intensity model of Bauwens & Hautsch (2006) to panel duration data.
We show how to estimate the model parameters by a simulated maximum likelihood technique adopting the efficient importance sampling approach of Richard & Zhang (2005). We provide an application to a trading activity dataset from an internet trading platform in the foreign exchange market and we find support for the presence of behavioral biases and discuss implications for portfolio theory.

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The model is based on a multivariate, fractionally integrated Autoregressive Moving Average (ARMA) process for the elements of the Cholesky factors of the observed matrix series. This approach allows for joint modelling of the whole covariance matrix and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions on the model. The model is particularly suited to capture the long memory, typically observed in volatility processes of financial assets. We describe the forecasting procedure and provide an empirical application.