Do institutions prevent contagion in financial markets? Evidence from the European debt crisis

ABSTRACT

The recent European Sovereign Debt Crisis brought in attention a number of structural problems in the European Union. Part of the effort to correct these problems in the countries that were mostly affected by the crisis were a number of policy responses from the European Union, the European Central Bank, the International Monetary Fund and the Local Governments. In this study, we attempt to assess the success of these responses to constrain the contagion of the crisis from the banking sector to the real economy sectors of the Eurozone countries. Our results show that policy announcements from the EU/ECB/IMF affect the transmission of shocks generated in the banking sector to the market. Moreover, policy responses of the national governments also seem to play a role in the contagion of the crisis.     

Forecasting high resolution electricity demand data with additive models including smooth and jagged components

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels.