Non – parametric estimation of conditional and unconditional loan portfolio loss distributions with public credit registry data

Employing a resampling-based Monte Carlo simulation developed in Carey (2000, 1998) and Majnoni, Miller and Powell (2004), in this paper we estimate conditional and unconditional loss distributions for loan portfolios of argentine banks in the period 1999-2004, controlling by type of borrower and type of bank. The exercise, performed with data contained in the public credit registry of the Central Bank of Argentina, yields economic estimates of expected and unexpected losses useful in bank supervision and in the prudential regulation of credit risk, for example to measure if Basel II’s IRB approach is appropriately calibrated to the local economy.Credit Risk, Unconditional loss distribution, Bootstrapping

Experimental investigation of the effect of an oscillating airstream (Katzmayr effect) on the characteristics of airfoils

A series of experiments were conducted related to the action of an airstream oscillating vertically on supporting surfaces. The object of the experiments was to verify the very interesting results of Mr. Katzmayr, Director of the Vienna Aerodynamics Laboratory, and, if possible, to obtain more complete data on the effect of the amplitude and velocity of the oscillations of the airstream. The results obtained by Mr. Katzmayr are briefly summarized. The conduct of the numerous experiments to verify his results are described in detail. Experimental results are given in tabular and graphical form

Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System

While not being widespread, stress tests of credit risk are not new in the Argentine financial system, neither for financial intermediaries nor for the Central Bank. However, they are more often based on rule-of-thumb approaches than on systematic, model based methodologies. The objective of this paper is to fill this gap. With a database that covers the 1994-2006 period we implement a three staged approach. First, we use bank balance sheet data to estimate a dynamic panel data model, with different statistical methodologies, to explain bank losses for credit risk with bank-specific and macroeconomic variables. In a second step, the macroeconomic drivers of bank losses, real GDP growth and cost of short term credit, are modeled with a Vector Autoregression (VAR). The VAR shows the effect of the variables (i.e. risk factors) that we find dominate the domestic business cycle: the price of commodities, the sovereign risk and the federal funds rate. Finally, we use this toolkit to perform deterministic and stochastic scenario analysis. In the first case we use the behavior of the risk factors during the crisis of 1995 (Tequila contagion) and 2001 (Currency Board collapse), and we implement a subjective scenario as well. The stochastic scenarios are performed by Monte Carlo with two alternative methodologies: a non-parametric bootstrapping approach and drawing repeatedly from a multivariate normal distribution. When comparing the estimated unexpected losses to available capital, we find that currently the Argentine financial system is adequately capitalized to absorb the higher losses that would take place in a stress situation.stress test; credit risk; dynamic panel data; Monte Carlo

Graph Variogram: A novel tool to measure spatial stationarity

Irregularly sampling a spatially stationary random field does not yield a graph stationary signal in general. Based on this observation, we build a definition of graph stationarity based on intrinsic stationarity, a less restrictive definition of classical stationarity. We introduce the concept of graph variogram, a novel tool for measuring spatial intrinsic stationarity at local and global scales for irregularly sampled signals by selecting subgraphs of local neighborhoods. Graph variograms are extensions of variograms used for signals defined on continuous Euclidean space. Our experiments with intrinsically stationary signals sampled on a graph, demonstrate that graph variograms yield estimates with small bias of true theoretical models, while being robust to sampling variation of the space.Comment: Submitted to IEEE Global Conference on Signal and Information Processing 2018 (IEEE GlobalSIP 2018), Nov 2018, Anaheim, CA, United States. (https://2018.ieeeglobalsip.org/

Public credit registries as a tool for bank regulation and supervision

This paper is about the importance of the information in Public Credit Registries (PCRs) for supporting and improving banking sector regulation and supervision, particularly in the light of the new approach embodied in Basel III. Against the backdrop of the financial crisis and the existence of information data gaps, the importance of complete, accurate and timely credit information in the financial system is evident. Both in normal times and during crises, authorities need a device that allows them to look at the universe of credits in a detailed and readily way. And more importantly, they need to develop tools that exploit as much as possible the information therein contained. PCR databases contain individual credit information on borrowers and their credits which makes it possible to implement advanced techniques that measure banks'credit risk exposure. It allows optimizing the prudential regulation ensuring that provisioning and capital requirements are properly calibrated to cover expected and unexpected losses respectively. It also permits validating banks'internal rating systems, performing stress tests and informing macroprudential surveillance. In this respect, it is envisioned that the existence of a PCR will be a key factor to enhance the supervision and regulation of the financial system. Furthermore, the extent, accuracy and availability of the information collected by the authorities will determine the usefulness of the PCR as part of their toolkit to monitor the potential vulnerabilities not only on a microprudential level, but also on a macroprudential one.Banks&Banking Reform,Access to Finance,Financial Intermediation,Debt Markets,Bankruptcy and Resolution of Financial Distress

Continuity properties of the inf-sup constant for the divergence

The inf-sup constant for the divergence, or LBB constant, is explicitly known for only few domains. For other domains, upper and lower estimates are known. If more precise values are required, one can try to compute a numerical approximation. This involves, in general, approximation of the domain and then the computation of a discrete LBB constant that can be obtained from the numerical solution of an eigenvalue problem for the Stokes system. This eigenvalue problem does not fall into a class for which standard results about numerical approximations can be applied. Indeed, many reasonable finite element methods do not yield a convergent approximation. In this article, we show that under fairly weak conditions on the approximation of the domain, the LBB constant is an upper semi-continuous shape functional, and we give more restrictive sufficient conditions for its continuity with respect to the domain. For numerical approximations based on variational formulations of the Stokes eigenvalue problem, we also show upper semi-continuity under weak approximation properties, and we give stronger conditions that are sufficient for convergence of the discrete LBB constant towards the continuous LBB constant. Numerical examples show that our conditions are, while not quite optimal, not very far from necessary

Variational formulation for a nonlinear elliptic equation in a three-dimensional exterior domain

An existence result was obtained for a nonlinear second-order equation in an exterior domain of IR(3). The proof relies on a variational formulation in weighted Sobolev spaces

New records of Elasmus (Hymenoptera, Eulophidae) species from Barrow Island, Western Australia

Eleven species of Elasmus are recorded from Barrow Island in northern Western Australia, including nine not previously recorded for Western Australia. Elasmus curticornis sp. n.is described as new to science

Generating Labels for Regression of Subjective Constructs using Triplet Embeddings

Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model training and testing. Current research trends focus on correcting artifacts and biases introduced by annotators during the annotation process while fusing them into a single annotation. In this work, we propose a novel annotation approach using triplet embeddings. By lifting the absolute annotation process to relative annotations where the annotator compares individual target constructs in triplets, we leverage the accuracy of comparisons over absolute ratings by human annotators. We then build a 1-dimensional embedding in Euclidean space that is indexed in time and serves as a label for regression. In this setting, the annotation fusion occurs naturally as a union of sets of sampled triplet comparisons among different annotators. We show that by using our proposed sampling method to find an embedding, we are able to accurately represent synthetic hidden constructs in time under noisy sampling conditions. We further validate this approach using human annotations collected from Mechanical Turk and show that we can recover the underlying structure of the hidden construct up to bias and scaling factors.Comment: 9 pages, 5 figures, accepted journal pape