Exposures and exposure hedging in exchange rate risk management

Corporations are affected by increasing volatilities on foreign exchange markets. A response to this development was the creation of financial instruments, so called derivatives, in order to protect corporations from the effects of flexible exchange rates. To understand the included risks and to take correct decisions it is necessary to get a fundamental insight into exchange rate risk management. First it is the aim of this paper to systemize the possibilities of determining exchange rate risk as well as objectives of exchange rate risk management. In the second part of the paper a model to determine the optimal hedge ratio in the case of hedging transaction risks with forwards is described. --Currency Risk,Transaction Risk,Currency Forwards,Optimal Hedging

The Dynamics of Google within the Frame of a Large Technical System: An LTS analysis of Google

The Large Technical System approach was introduced by the influential historian of technology, Thomas P. Hughes, in the 1970’s and is one of the most prominent theoretical frameworks within the Science and Technology Studies. However, it has found little attention in relation to the digital realm. This research applies the LTS framework onto the US-American company Google and seeks to bring a conceptual understanding to the company’s exponential growth. Thus, it describes the emergence and evolution of Google as a complex system – an alignment of components of technical and non-technical nature – and assigns patterns and concepts to its development. This research provides an answer to how Google not only gained a system structure but also reached the notion of momentum. Yet, suggesting a social constructivist path, this paper secludes by elucidating the influencing power of the LTS’s user – an important factor which was widely disregarded in the initial works of Hughes

Script: Usability of Hand & Wrist Tele-Rehabilitation for Stroke Patients Involving Personal Tele-Robotics

We present the overall user experience designed for supporting rehabilitation of stroke patients at home. Patients use a robotic hand (orthosis) to control therapeutic games and a touch screen for the UI. They are supervised remotely by a therapist who uses a similar interface from their desk. The system includes therapeutic games and user interfaces (UIs) for both patients and therapists. The concept and design of these UIs were implemented during the first year of the SCRIPT projectPeer reviewe

Secondary control activation analysed and predicted with explainable AI

The transition to a renewable energy system poses challenges for power grid operation and stability. Secondary control is key in restoring the power system to its reference following a disturbance. Underestimating the necessary control capacity may require emergency measures, such as load shedding. Hence, a solid understanding of the emerging risks and the driving factors of control is needed. In this contribution, we establish an explainable machine learning model for the activation of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate description of control activation. Using SHapely Additive exPlanation (SHAP) values, we investigate the dependency between control activation and external features such as the generation mix, forecasting errors, and electricity market data. Thereby, our analysis reveals drivers that lead to high reserve requirements in the German power system. Our transparent approach, utilizing open data and making machine learning models interpretable, opens new scientific discovery avenues.Comment: 8 pages, 6 figure

Semi-automating the reading programme for a historical dictionary project

This paper describes the resources and software procedures used or developed in a major enabling step towards the revision of the scholarly reference work A  Dictionary of South African English on Historical Principles (DSAE, Silva et al. 1996), namely the semi-automatic generation of a digitally-sourced lexical database on which new and updated dictionary entries will be based; as well as the addition, in parallel, of a new corpus of South African English (SAE) to the project. Drawing on online data sources and an extensive list of known SAE word forms, we have developed a software toolchain to gather, encode, annotate and collate textual sources, producing: (i) a 3.1-billion part-of-speech-annotated corpus of South African English; (ii) a lexical database of illustrative quotations for over 20,000 known SAE word forms, available for selection at the entry-revision stage; and (iii) a list of potential new variant spellings and headword inclusion candidates. These steps replace, where recent electronic sources are concerned, the mechanical aspects of quotation gathering, normally undertaken manually through a reading programme requiring years of teamwork to acquire sufficient coverage (cf. Hicks 2010).Keywords: corpora, dictionary workflows, historical lexicography, language varieties, lexical databases, reading programmes, South African Englis

Physics-inspired machine learning for power grid frequency modelling

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers.Comment: 21 pages, 5 figure