What is the Minimal Systemic Risk in Financial Exposure Networks?

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased. It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empirical ones have been studied by [pichler2018]. Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topology, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk [poledna2017]. Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in [thurner2013] and [poledna2016]. For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets.Comment: 25 page

cito: An R package for training neural networks using torch

Deep Neural Networks (DNN) have become a central method for regression and classification tasks. Some packages exist that allow to fit DNN directly in R, but those are rather limited in their functionality. Most current deep learning applications rely on one of the major deep learning frameworks, in particular PyTorch or TensorFlow, to build and train DNNs. Using these frameworks, however, requires substantially more training and time than typical regression or machine learning functions in the R environment. Here, we present 'cito', a user-friendly R package for deep learning that allows to specify deep neural networks in the familiar formula syntax used in many R packages. To fit the models, 'cito' uses 'torch', taking advantage of the numerically optimized torch library, including the ability to switch between training models on CPUs or GPUs. Moreover, 'cito' includes many user-friendly functions for model plotting and analysis, including optional confidence intervals (CIs) based on bootstraps on predictions as well as explainable AI (xAI) metrics for effect sizes and variable importance with CIs and p-values. To showcase a typical analysis pipeline using 'cito', including its built-in xAI features to explore the trained DNN, we build a species distribution model of the African elephant. We hope that by providing a user-friendly R framework to specify, deploy and interpret deep neural networks, 'cito' will make this interesting model class more accessible to ecological data analysis. A stable version of 'cito' can be installed from the comprehensive R archive network (CRAN).Comment: 15 pages, 4 figures, 2 table

What is the Minimal Systemic Risk in Financial Exposure Networks? INET Oxford Working Paper, 2019-03

Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic risk levels might not have lowered, but even increased (see ECB data 1 ; SRISK time series 2 ). It has been shown that systemic risk is to a large extent related to the underlying network topology of financial exposures. A natural question arising is how much systemic risk can be eliminated by optimally rearranging these networks and without increasing capital requirements. Overlapping portfolios with minimized systemic risk which provide the same market functionality as empir- ical ones have been studied by Pichler et al. (2018). Here we propose a similar method for direct exposure networks, and apply it to cross-sectional interbank loan networks, consisting of 10 quarterly observations of the Austrian interbank market. We show that the suggested framework rearranges the network topol- ogy, such that systemic risk is reduced by a factor of approximately 3.5, and leaves the relevant economic features of the optimized network and its agents unchanged. The presented optimization procedure is not intended to actually re-configure interbank markets, but to demonstrate the huge potential for systemic risk management through rearranging exposure networks, in contrast to increasing capital requirements that were shown to have only marginal effects on systemic risk (Poledna et al., 2017). Ways to actually incentivize a self-organized formation toward optimal network configurations were introduced in Thurner and Poledna (2013) and Poledna and Thurner (2016). For regulatory policies concerning financial market stability the knowledge of minimal systemic risk for a given economic environment can serve as a benchmark for monitoring actual systemic risk in markets

Das Wasserkraftwerk Freudenau

Täglich verbrauchen wir mehr endliche Ressourcen als uns die Erde zur Verfügung stellt. Daher ist es an der Zeit unsere Gewohnheiten anzupassen und auf fossile Brennstoffe wie Öl, Kohle oder Gas als Energielieferanten weitgehend zu verzichten. Da Atomenergie umstritten ist, gewinnen erneuerbare Energiequellen wie Wasser-, Windkraft, Biomasse oder Photovoltaik immer mehr an Bedeutung. Diese Formen der erneuerbaren Energiegewinnung spielen in Zukunft eine zentrale Rolle und müssen in den Regionen ausgebaut werden, in denen sie vorhanden sind und effizient genützt werden können. Auf Grund seiner vielen Berge und Flüsse ist Wasserkraft, speziell in Österreich, im Jahre 2011 die wichtigste erneuerbare Energiegewinnungsform – sie trägt zirka 60% zur gesamten heimischen Stromerzeugung bei (Verbund 2010a: 24). Gleichzeitig gilt sie als die wirtschaftlichste und wettbewerbsfähigste der erneuerbaren Energiequellen, aber ist sie auch ökologisch verträglich? Tatsächlich gibt es Unterschiede zwischen den Wasserkraftwerken in Österreich, gemäß ihrer Umwelt-, Wirtschafts- und Sozialverträglichkeit. Im Rahmen dieser Diplomarbeit wird das Werk Freudenau als Beispiel für österreichische Laufwasserkraftwerke herangezogen und anhand von ökologischen, ökonomischen und sozialen Kategorien bewertet, nach welchen Aspekten es den Prinzipien der Nachhaltigkeit entspricht. Diese lauten: Nahrung in Form von Fischen, Biodiversität, Wasserversorgung, Kommunikation und Partizipation, elektrische Energie, Wasser als Transportmedium und die Wirtschaftlichkeit des Kraftwerkes. In dieser Arbeit wird auch das Konzept der Ökosystemdienstleistungen (ecosystem services) integriert, das die Auswahl der zu untersuchenden Kategorien maßgeblich erleichtert hat. Um die Analyse in Relation zu setzen, wurde ein Experteninterview-geführter Ansatz angewendet, dafür konnten Experten aus den Bereichen Umwelt, Gesellschaft und Wirtschaft für das Projekt gewonnen werden. Die vorliegende Diplomarbeit beschäftigt sich also mit drei zentralen Themen: Wasserkraft, Ecosystem services und Nachhaltigkeit. Zusammengefasst zeigt sie, dass das Laufkraftwerk Freudenau ökologischen, ökonomischen und sozialen Aspekten der Nachhaltigkeit entspricht – die Hypothese des Theorieteils wird bestätigt. Bei den wirtschaftlichen und gesellschaftlichen Kategorien versteht das Projekt Freudenau besonders zu überzeugen und zeigt, dass das Wasserkraftwerk Freudenau sowohl volks-, als auch betriebswirtschaftlich rentabel ist. Darüber hinaus erfüllt es gesellschaftliche Ansprüche wie Stromproduktion, Bevölkerungsbeteiligung, Sicherheit für die Schifffahrt und Wasserversorgung. Vom ökologischen Standpunkt ist das Laufkraftwerk Freudenau jedoch diskussionswürdig. Die Fischaufstiegshilfe stellt zwar eine gewisse Konnektivität zwischen Ober- und Unterwasser her, die vorliegende Analyse zeigt jedoch, dass das Umgehungsgerinne unbedingt modifiziert werden muss. Außerdem müssen die Geschwindigkeiten der Donauschiffe besser an ökologische Anforderungen angepasst werden - in diesem Punkt herrscht akuter Handlungsbedarf. Generell zeigt diese Diplomarbeit, dass man anhand schadensminimierender Begleitmaßnahmen wie der Fischaufstiegshilfe, der Grundwasserbewirtschaftung oder der Stauraumneugestaltung negative ökologische Auswirkungen beim Laufkraftwerk Freudenau minimieren konnte. Bei zukünftigen Wasserkraftwerksprojekten muss es Ziel sein, die angesprochenen Limitationen des Projektes Freudenau zu beseitigen und damit mögliche ökologische Schäden zu mindern. Daher ist es einerseits essentiell, aus Betriebserfahrungen bestehender Wasserkraftwerke zu lernen und andererseits die Funktionsfähigkeit eines derartigen Großprojektes durch regelmäßige Berichte laufend zu überprüfen und zu bewerten. Die vorliegende Arbeit zeigt auch, dass Wasserkraft derzeit als eine ökonomisch effiziente Form der erneuerbaren Energiegewinnung anzusehen ist, die darüber hinaus gesellschaftliche Nutzungsansprüche erfüllen kann. Die ökologischen Begleitmaßnahmen gehören jedoch weiter verbessert, um Wasserkraft als „Die Zukunftsträchtige Energieversorgung“ global zu etablieren

Photoreforming of biomass in metal salt hydrate solutions.

Metal salt hydrate (MSH) solutions allow for the complete solubilisation of biomass and we demonstrate its use as a reaction medium for the photocatalytic reforming of lignocellulose. Different types of photocatalysts such as TiO2 and carbon nitride can be employed in MSH to produce H2 and organic products under more benign conditions than the commonly required extreme pH aqueous solutions

Conversion of Polyethylene Waste into Gaseous Hydrocarbons via Integrated Tandem Chemical-Photo/Electrocatalytic Processes.

The chemical inertness of polyethylene makes chemical recycling challenging and motivates the development of new catalytic innovations to mitigate polymer waste. Current chemical recycling methods yield a complex mixture of liquid products, which is challenging to utilize in subsequent processes. Here, we present an oxidative depolymerization step utilizing diluted nitric acid to convert polyethylene into organic acids (40% organic acid yield), which can be coupled to a photo- or electrocatalytic decarboxylation reaction to produce hydrocarbons (individual hydrocarbon yields of 3 and 20%, respectively) with H2 and CO2 as gaseous byproducts. The integrated tandem process allows for the direct conversion of polyethylene into gaseous hydrocarbon products with an overall hydrocarbon yield of 1.0% for the oxidative/photocatalytic route and 7.6% for the oxidative/electrolytic route. The product selectivity is tunable with photocatalysis using TiO2 or carbon nitride, yielding alkanes (ethane and propane), whereas electrocatalysis on carbon electrodes produces alkenes (ethylene and propylene). This two-step recycling process of plastics can use sunlight or renewable electricity to convert polyethylene into valuable, easily separable, gaseous platform chemicals

An Automated Verification Workflow for Planned Lighting Setups using BIM

The use of Building Information Modeling (BIM) methods is becoming more and more established in the planning stage, during the construction, and for the management of buildings. Tailored BIM software packages allow to handle a vast amount of relevant aspects, but have so far not been covering specialized tasks like the evaluation of light distributions in and around a 3D model of a building. To overcome this limitation, we demonstrate the use of the open-source IFC format for preparing and exchanging BIM data to be used in our interactive light simulation system. By exploiting the availability of 3D data and semantic descriptions, it is possible to automatically place measurement surfaces in the 3D scene, and evaluate the suitability and sustainability of a planned lighting design according to given constraints and industry norms. Interactive visualizations for fast analysis of the simulation results, created using state-of-the-art web technologies, are seamlessly integrated in the 3D work environment, helping the lighting designer to quickly improve the initial lighting solution with a few clicks