Measuring and managing the credit exposure of derivatives portfolios

CONCLUSION The analysis of the exposure measurement problem has shown that the proper measurement of counterparty exposure for portfolios of derivatives transactions is a complex task that cannot be performed without making a lot of simplifying assumptions. Because of the complicated interaction of correlation effects and offsettings from different transactions, the single transaction framework which is currently used by most banks is definitely not capable of accurately determining the portfolio credit risk. When simulation techniques are applied to estimate exposure, the accuracy of exposure estimations can be increased significantly. However, a lot of modelling choices has to be made concerning the valuation of transactions and the stochastic model of underlying market rates. Because the system has to make projections of market rates into the far future, the choice of an appropriate stochastic model for market rate dynamics is crucial in order to prevent unreasonable scenarios. The predominant application of models based on Brownian Motion in today’s bank risk management therefore leads to questionable results in respect to derivatives exposure evaluation

Dynamic Models of Appraisal Networks Explaining Collective Learning

This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.Comment: A preliminary version has been accepted by the 53rd IEEE Conference on Decision and Control. The journal version has been submitted to IEEE Transactions on Automatic Contro

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI