GDX öffnet Werkzeugschleifern die Tür ins Web : offene Datenschnittstelle deckt den gesamten Produktlebenszyklus ab

Die Bereitstellung der für einen Zerspanungsvorgang relevanten Werkzeugdaten ist heute selbstverständlicher Bestandteil integrierter Fertigungssysteme. Die für die Herstellung und das Nachschärfen der eingesetzten Zerspanungswerkzeuge notwendigen Informationen bleiben dabei allerdings bisher unberücksichtigt. Diese Lücke will eine VDI-Arbeitsgruppe schließen. Das Ergebnis wird in Form einer offenen Datenschnittstelle auf der bevorstehenden Grindtec 2014 präsentiert

Advancing Systematic and Factor Investing Strategies using Alternative Data and Machine Learning

This thesis advances systematic and factor investing strategies using alternative data and machine learning techniques. The first chapter studies the relevance of high-frequency news data for low-frequency factor investing strategies. We build various news-based equity factors for an investable global equity universe to investigate the factors’ ability to extend the information inherent in standard factor models. Specifically, we document that incorporating news-based equity factors benefits multi-factor equity investments, employing diversified multi-factor equity allocations but also more dynamic factor timing strategies. The second chapter examines dynamic asset allocation strategies that focus on explicit downside risk management. We investigate suitable risk models that best inform tail risk protection strategies. In addition to forecasting portfolio risk based on standalone models such as extreme value theory or copula-GARCH, we propose a novel expected shortfall (ES) and value-at-risk (VaR) forecast combination approach that utilizes a loss function that overcomes the lack of elicitability for ES. This forecast combination method dominates simple and sophisticated standalone models as well as a simple average combination approach in terms of statistical accuracy. While the associated dynamic risk targeting or portfolio insurance strategies provide effective downside protection, the latter strategies suffer less from inferior risk forecasts, given the defensive portfolio insurance mechanics. The third chapter extends the above ES and VaR forecast combination approach using machine learning techniques. Building on a rich predictor set of VaR and ES forecasts from an array of econometric models (including GARCH, CAViaR-EVT, dynamic GAS and realized range models), we leverage shrinkage and neural network models to form combination forecasts. Such machine-learned VaR and ES forecasts outperform a set of competing forecast combination approaches in terms of statistical accuracy as well as economical relevance in dynamic tail risk protection strategies

How to rein in fossil fuel subsidies? : towards a New WTO Regime

Published online: 12 August 2021Fossil fuel subsidies have negative consequences on the climate change, public budgets and and the transition to an environmentally friendly economy. Nevertheless, governments do not keep up with their commitments to phase out fossil fuel subsidies but misallocate again COVID-19 recovery funds in fossil fuel subsidies. This article provides an analysis of the current obstacles for phasing out fossil fuel subsidies and the potential of the WTO to advance a reform on fossil fuel subsidies. It argues that the WTO can contribute to a fossil fuel subsidies reform by its technical expertise in regulating subsidies, by its broad membership and by its institutional setting. Under the current framework of the ASCM, WTO member can use existing mechanisms, such as the TPRM, to increase transparency in the short term and facilitate discussions on the scope of subsidies while mitigating impacts on vulnerable groups or sectors. This would provide the ground for governments to work towards a new and ambitious agreement to stop producer fossil fuels subsidies and phase out consumer fossil fuels subsidies in the mid-to-long-term. However, the phase out of consumer subsidies needs to be carefully designed and embedded, to avoid unintended consequences on energy access and vulnerable households