Modeling the evolution of implied CDO correlations

CDO tranche spreads (and prices of related portfolio-credit derivatives) depend on the market’s perception of the future loss distribution of the underlying credit portfolio. Applying Sklar’s seminal decomposition to the distribution of the vector of default times, the portfolio-loss distribution derived thereof is specified through individual default probabilities and the dependence among obligors’ default times. Moreover, the loss severity, specified via obligors’ recovery rates, is an additional determinant. Several (specifically univariate) credit derivatives are primarily driven by individual default probabilities, allowing investments in (or hedging against) default risk. However, there is no derivative that allows separately trading (or hedging) default correlations; all products exposed to correlation risk are contemporaneously also exposed to default risk. Moreover, the abstract notion of dependence among the names in a credit portfolio is not directly observable from traded assets. Inverting the classical Vasicek/Gauss copula model for the correlation parameter allows constructing time series of implied (compound and base) correlations. Based on such time series, it is possible to identify observable variables that describe implied correlations in terms of a regression model. This provides an economic model of the time evolution of the market’s view of the dependence structure. Different regression models are developed and investigated for the European CDO market. Applications and extensions to other markets are discussed.     

Time Series Modelling of Daily Tax Revenues

We provide a detailed discussion of time series modelling of daily data in general and daily tax revenues in particular. The main feature of the daily tax revenue series is the pattern within calendar months. Standard time series methods for seasonal adjustment and forecasting cannot be used since the number of banking days per calendar month varies and because there are two levels of seasonality: between months and within months. We propose a daily time series model based on unobserved components that allows for the classic decomposition into trend, seasonal plus irregular, but it also includes components for intra-monthly, trading-day and length-of-month effects. Such components typically rely on stochastic cubic spline, polynomial and dummy variable functions. State space techniques are used for the recursive computation of the likelihood and forecasts functions with special allowance for irregular spacing. The model is operational for daily forecasting at the Dutch Ministry of Finance. We present the model specification and discuss estimation and forecasting results up to December 1999. A comparative forecast evaluation is also presented.     

Sectoral Patterns of Interactive Learning: An Empirical Exploration of a Case in a Dutch Region

This paper pursues the development of a theoretical framework that explains interactive learning between innovator firms and external actors in both the knowledge infrastructure and the production chain. The research question is: What kinds of factors explain the interactive learning of innovator firms with distinct external actors? Our theoretical framework extends the resource-based perspective, which is predominant in network theory, with both an activity-based and a structural account of interactive learning. We contend basically that higher technological dynamics induce more complex innovative activities. But, more complex innovative activities increase the probability of internal resource deficits/shortages in the innovator firms. The lower the alignment of the innovative activities with the quality of the internal resource base, the higher the resource deficits/shortages and the more likely the search for complementary resources externally, which increases the likeliness of external relationships. In order to test the generality of our theoretical claims we analyse our models in four sectors with distinct technological dynamics as distinguished by Pavitt. For each sector we explore five models of the level of interactive learning of innovator firms with: (1) the public knowledge infrastructure (difficult to access, demands high internal competencies to utilize scientific knowledge), (2) the production chain (easy to access), (3) their users, (4) their suppliers, (5) their competitors. These analyses allow for a comparison between interactive learning with different external actors and give deeper insights into the differentiated interaction patterns involving innovation. Our findings show that patterns of interactive learning between sectors differ. Some are more resource based and others are more affected by the complexity of innovative activities. Particularly the patterns of interactive learning between, on the one hand, firms and the knowledge infrastructure, and on the other hand of firms with the production chain show important differences.     

Social Security,Unemployment, and Growth

The paper develops an overlapping generations model that highlights interactions between social security, unemployment and growth. The social security system has two components: old age pensions and unemployment insurance. Pensions have a direct effect on economic growth. Both pensions and unemployment benefits influence equilibrium unemployment caused by wage bargaining. Since unemployment deteriorates growth, both types of social security have an indirect negative effect on growth.JEL Code: E24, H55, J51     

Nachhaltigkeit von Biokraftstoffen: Ziele,Probleme und Instrumente

Klimawandel und Versorgungssicherheit bestimmen die Suche nach ?kologisch und ?konomisch nachhaltigen Energietr?gern. Biokraftstoffe scheinen beiden Zielen gleicherma?en gerecht zu werden. Was ist bei der Bewertung der Nachhaltigkeit von Biokraftstoffen zu beachten? Sollte der Staat bei der Bereitstellung eingreifen? Welche wirtschaftspolitischen Instrumente schaffen positive Anreize beim Verbrauch von Biokraftstoffen? Dr. Michael Br?uninger, 43, ist Leiter des Kompetenzbereichs Wirtschaftliche Trends des Hamburgischen WeltWirtschaftsInstituts HWWI; Leon Leschus, 31, Dipl.-Volkswirt, und Dr. Henning V?pel, 35, sind dort wissenschaftliche Mitarbeiter.     

Forecasting Euro‐Area Macroeconomic Variables Using a Factor Model Approach for Backdating

We suggest to use a factor model based backdating procedure to construct historical Euro‐area macroeconomic time series data for the pre‐Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The article investigates for a number of Euro‐area variables whether forecasts based on the factor‐backdated data are more precise than those obtained with standard area‐wide data. A recursive pseudo‐out‐of‐sample forecasting experiment using quarterly data is conducted. Our results suggest that some key variables (e.g. real GDP, inflation and long‐term interest rate) can indeed be forecasted more precisely with the factor‐backdated data.