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[Tagungsbericht] Making finance sustainable: Ten years equator principles – success or letdown?
(2013)
In 2003, a number of banks adopted the Equator Principles (EPs), a voluntary Code of Conduct based on the International Finance Corporation’s (IFC) performance standards, to ensure the ecological and social sustainability of project finance. These so called Equator Principles Financial Institutions (EPFI) commit to requiring their borrowers to adopt sustainable management plans of environmental and social risks associated with their projects. The Principles apply to the project finance business segment of the banks and cover projects with a total cost of US $10 million or more. While for long developing countries relied on World Bank and other public assistance to finance infrastructure projects there has occurred a shift in recent years to private funding. The NGOs have been frustrated by this shift of project finance as they had spent their resources to exercise pressure on the public financial institutions to incorporate environmental and social standards in their project finance activities. However, after a shift of NGO pressure to private financial institutions the latter adopted the EPs for fear of reputational risks. NGOs had laid down their own more ambitious ideas about sustainable finance in the Collevecchio Declaration on Financial Institutions and Sustainability. Legally speaking, the EPs are a self-regulatory soft law instrument. However, it has a hard law dimension as the Equator Banks require their borrowers to comply with the EPs through covenants in the loan contracts that may trigger a default in a case of violation. ...
Augmented reality (AR) gained much public attention since the success of Pok´emon Go in 2016. Technology companies like Apple or Google are currently focusing primarily on mobile AR (MAR) technologies, i.e. applications on mobile devices, like smartphones or tablets. Associated privacy issues have to be investigated early to foster market adoption. This is especially relevant since past research found several threats associated with the use of smartphone applications. Thus, we investigate two of the main privacy risks for MAR application users based on a sample of 19 of the most downloaded MAR applications for Android. First, we assess threats arising from bad privacy policies based on a machine-learning approach. Second, we investigate which smartphone data resources are accessed by the MAR applications. Third, we combine both approaches to evaluate whether privacy policies cover certain data accesses or not. We provide theoretical and practical implications and recommendations based on our results.
This paper provides an assessment framework for privacy policies of Internet of Things Services which is based on particular GDPR requirements. The objective of the framework is to serve as supportive tool for users to take privacy-related informed decisions. For example when buying a new fitness tracker, users could compare different models in respect to privacy friendliness or more particular aspects of the framework such as if data is given to a third party. The framework consists of 16 parameters with one to four yes-or-no-questions each and allows the users to bring in their own weights for the different parameters. We assessed 110 devices which had 94 different policies. Furthermore, we did a legal assessment for the parameters to deal with the case that there is no statement at all regarding a certain parameter. The results of this comparative study show that most of the examined privacy policies of IoT devices/services are insufficient to address particular GDPR requirements and beyond. We also found a correlation between the length of the policy and the privacy transparency score, respectively.
Rating agencies state that they take a rating action only when it is unlikely to be reversed shortly afterwards. Based on a formal representation of the rating process, I show that such a policy provides a good explanation for the puzzling empirical evidence: Rating changes occur relatively seldom, exhibit serial dependence, and lag changes in the issuers’ default risk. In terms of informational losses, avoiding rating reversals can be more harmful than monitoring credit quality only twice per year.
Mobile telephony and mobile internet are driving a new application paradigm: location-based services (LBS). Based on a person’s location and context, personalized applications can be deployed. Thus, internet-based systems will continuously collect and process the location in relationship to a personal context of an identified customer. One of the challenges in designing LBS infrastructures is the concurrent design for economic infrastructures and the preservation of privacy of the subjects whose location is tracked. This presentation will explain typical LBS scenarios, the resulting new privacy challenges and user requirements and raises economic questions about privacy-design. The topics will be connected to “mobile identity” to derive what particular identity management issues can be found in LBS.
Eine spieltheoretische Analyse von Zulieferer-Abnehmer-Beziehungen auf Basis des JELS-Modells
(2001)
Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
US investors hold much less foreign stocks than mean/variance analysis applied to historical data predicts. In this article, we investigate whether this home bias can be explained by Bayesian approaches to international asset allocation. In contrast to mean/variance analysis, Bayesian approaches employ different techniques for obtaining the set of expected returns. They shrink sample means towards a reference point that is inferred from economic theory. We also show that one of the Bayesian approaches leads to the same implications for asset allocation as mean-variance/tracking error criterion. In both cases, the optimal portfolio is a combination the market portfolio and the mean/variance efficient portfolio with the highest Sharpe ratio.
Applying the Bayesian approaches to the subject of international diversification, we find that substantial home bias can be explained when a US investor has a strong belief in the global mean/variance efficiency of the US market portfolio and when he has a high regret aversion falling behind the US market portfolio. We also find that the current level of home bias can justified whenever regret aversion is significantly higher than risk aversion.
Finally, we compare the Bayesian approaches to mean/variance analysis in an empirical out-ofsample study. The Bayesian approaches prove to be superior to mean/variance optimized portfolios in terms of higher risk-adjusted performance and lower turnover. However, they not systematically outperform the US market portfolio or the minimum-variance portfolio.