Efficient compliance checking of RDF data

Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets

Estimating Portfolio Risk for Tail Risk Protection Strategies

We forecast portfolio risk for managing dynamic tail risk protection strategies, based on extreme value theory, expectile regression, Copula-GARCH and dynamic GAS models. Utilizing a loss function that overcomes the lack of elicitability for Expected Shortfall, we propose a novel Expected Shortfall (and Value-at-Risk) forecast combination approach, which dominates simple and sophisticated standalone models as well as a simple average combination approach in modelling the tail of the portfolio return distribution. 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