Credit and liquidity risk of banks in stress conditions : analyses from a macro perspective

This thesis brings together research on credit and liquidity risks of banks in stress conditions. It investigates banks’ reactions to those risks, presents macro stress-testing models and analyses policy measures to contain the risks during the 2007-2009 financial crisis. First we analyse how Dutch banks adjusted their credit and liquidity risk management during the crisis by empirical indicators and time series models. The results provide evidence on the time and cross-sectional dimensions of bank behaviour and on banks’ responses to funding liquidity shocks. Second, we model the impact on banks of tail events that involve credit and liquidity risk and banks’ reactions to those risks in a stress-testing framework. The framework is operationalised by a suite of models, such as reduced form satellite models, vector autoregressive (VAR) models and calibrated simulation tools. We show that shocks to the liquidity position of banks entail systemic risk through behavioural responses and that tail risks of stress scenarios are substantially lower if banks would adjust to Basel III. Third we analyse the policy responses to the credit and liquidity risks of banks in the crisis, by assessing the short-term crisis measures taken by central banks and governments in 2007-2009 and the macroeconomic effects of Basel III. Simulation outcomes of reduced form satellite models and a structural macroeconomic model indicate that the negative impact of Basel III on real GDP will be limited and be outweighed by the benefits in the new steady state.

Holmium Laser Enucleation of the Prostate

Introduction: Holmium laser enucleation of the prostate (HoLEP) offers superior voiding outcomes to traditional transurethral resection and less morbidity than open simple prostatectomy. Likewise, HoLEP has been determined to result in excellent outcomes regardless of gland size. We present a step-by-step surgical approach to HoLEP describing both the traditional enucleation technique and a modified “top-down” surgical technique. Materials and Methods: In this video, two techniques are presented that were performed by two (A.E.K., J.E.L.) surgeons at our institution. Results: In the examples of the two enucleation techniques mentioned, outcomes are similar with regard to surgical and functional outcomes. Conclusions: HoLEP as a treatment for BPH with associated lower urinary tract symptoms (LUTS) results in excellent patient outcomes and can be offered to patients regardless of prostate volume

End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

End-to-end Neural Coreference Resolution

We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.Comment: Accepted to EMNLP 201