Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear...

The paper analyzes non-negative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model...

We investigate the importance of taking the spatial interaction of turbines inside a wind park into account. This article provides two tests that check for wake effects and thus, take spatial interdependence into account. Those effects are suspected to have a negative influence on wind power...

Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new...

VARs are a popular tool for forecasting and structural analysis, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as censored observations of a latent shadow-rate process,...

This paper uses several macroeconomic and financial indicators within a Markov Switching (MS) framework to predict the turning points of the business cycle. The presented model is applied to monthly German real-time data covering the recession and the recovery after the financial crisis. We show...

The power system has to deal with three main sources of uncertainty: demand uncertainty and load prediction errors, failure of power plants and uncertainty of wind. The growing share of wind and other intermittent generation sources in the European supply increases the uncertainty about power...

This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose...

Volatility-based filtering is proposed to pre-process historical daily return data of stock indexes before applying to price-based technical analysis trading rules. Any “nearly flat” days which have daily gains or losses less than a threshold about 20% of a daily volatility measure, is...

This paper uses several macroeconomic and financial indicators within a Markov Switching (MS) framework to predict the turning points of the business cycle. The presented model is applied to monthly German real-time data covering the recession and the recovery after the financial crisis. We show...