It is well known that the correlation between financial series varies over time. Here, the forecasting performance of different time-varying correlation models is compared for cross-country correlations of weekly G5 and daily European stock market indices. In contrast to previous studies only...

This study focuses on the question whether nonlinear transformation of lagged time series values and residuals are able to systematically improve the average forecasting performance of simple Autoregressive models. Furthermore it investigates the potential superior forecasting results of a...

This paper reinterprets Maganelli's (2009) idea of "Forecasting with Judgment" to obtain a dynamic algorithm for combining survey data and time series models for macroeconomic forecasting. Unlike existing combination approaches which typically assign weights to alternative forecasts, the...

Correlations are the main drivers for credit portfolio risk and constitute a Major element in pricing credit derivatives such as synthetic single-tranche collateralized debt obligation swaps. This paper suggests a dynamic panel regression Approach to model and forecast implied correlations....

We explore the efficiency in the oil industry employing a sample of around 300 companies operating in Europe over 2010-2019. We construct efficiency scores by means of DEA non-parametric techniques. Average efficiency in the sample is modest, 0.27 and decreasing over time. We examine the...

This study investigates the time-varying frequency of spillovers between European stock markets and oil during the COVID-19 pandemic and the Russia-Ukraine war. Using the spillover index by Diebold & Yilmaz, 2012 and Baruník & Křehlík, 2018, we analyze high-frequency data at a 5-min interval...

The dynamics of hourly electricity prices in day-ahead markets is an important element of competitive power markets that were only established in the last decade. In electricity markets, the market microstructure does not allow for continuous trading, since operators require advance notice in...

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci.cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coeØ cients, whereas...

We develop distress prediction models for non-financial small and medium sized enterprises (SMEs) using a dataset from eight European countries over the period 2000-2009. We examine idiosyncratic and systematic covariates and find that macro conditions and bankruptcy codes add predictive power...

The present study investigates the performance of the k nearest neighbor (kNN) forecasts in the context of European tourism demand. The forecasting performance of neural networks is examined across different parameterizations of the kNN model. The selection of the most appropriate kNN...