We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a...

The Black-Litterman (BL) model aims to enhance asset allocation decisions by overcoming the weaknesses of standard mean-variance (MV) portfolio optimization. In this study we implement the BL model in a multi-asset portfolio context. Using an investment universe of global stock indices, bonds,...

This paper considers how an investor in foreign exchange markets might exploit predictive information in macroeconomic fundamentals by allowing for switching between multivariate time series regression models. These models are chosen to reflect a wide array of established empirical and...

The present paper considers urban sprawl in Europe from a multi- dimensional and exploratory point of view. Several literatures on the topic (mainly from urban economics, but also from other fields such as urban planning or regional science) are reviewed to extract the main dimensions of the...

The present contribution develops on the analysis of clusters in terms of proximities by exploring the issue of distant inter-cluster collaborations. We mobilize different forms of proximity (geographic, cognitive, social) discussed in the literature in order to identify their respective...

This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that articial neural networks are able to predict the nominal interest rate better than linear and nonlinear Taylor rule models as...

High rates of student attrition in tertiary education are a major concern for universities and public policy, as dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is imperative to understand which students are at risk of...

Data Mining represents a Business Intelligence (BI) methodology which provides an insight into the 'hidden' information about its operations thus improving the process of making strategic business decisions based on a clear and understandable interpretation of existing results. Data mining can...

The aim of this paper is comparison of multivariate statistical analysis and machine learning methods based on the model used for the measurement of current and forecasting of the future customer profitability. Modern customer profitability analysis shows that customer-company relationship is...