Suchergebnisse

ABSTRACTThe prices of cash crops impact the livelihoods of millions of households in developing countries. While the influence of speculators on global commodity prices determined through derivatives exchanges is extensively discussed, the contribution of hedgers to short‐term changes in futures prices has largely been disregarded in the financialization of commodities discourse over the past two decades. This results in a failure to account for the interconnected activities of increasingly consolidated lead firms within physical global value chains (GVCs) and derivatives markets. This article examines the pricing and hedging strategies of lead firms in the coffee, cocoa and cotton GVCs in relation to their activities on commodity derivatives markets. Based on Open Interest data as an indicator of derivatives markets activity, a measure of buying and selling pressure by trader categories is applied in a Generalized ARCH (GARCH) model. The findings of this article show that hedgers' activities allow speculators to drive global benchmark prices so that they can benefit through combinations of financial hedging and physical trading strategies. As these practices of lead firms contribute to the transmission of futures prices along GVCs, smallholders and other actors in cash crops in producer countries are exposed to heightened price changes.

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

We examine the determinants of the net interest margin (NIM) and the net fee and commission income ratio (NFCIR) of Austrian banks as well as their interrelationship and whether portfolio separation between loan and deposit categories holds. We describe a conceptual framework for the profit optimization problem faced by banks as a Bertrand game with differentiated products and intrafirm product interactions. We contribute to the literature by factoring in banks' business models in terms of their balance sheet structure. We empirically assess the implications of our conceptual framework using a unique supervisory data set of around 48,000 observations between 1998 and 2014. We estimate two panel vector autoregression models with a novel panel vector autoregression code. Apart from quantifying the contributions of the determinants (e.g., risk weighted assets, leverage ratio, loan loss provision ratio) to NIM and NFCIR, the empirical results show that interest income and fee and commission income should be regarded as strategic complements within a bank. We further conclude that portfolio separation between different loan and deposit categories does not hold.