Adjustable network reconstruction with applications to CDS exposures

This paper is concerned with reconstructing weighted directed networks from the total in- and out-weight of each node. This problem arises for example in the analysis of systemic risk of partially observed financial networks. Typically a wide range of networks is consistent with this partial information. We develop an empirical Bayesian methodology that can be adjusted such that the resulting networks are consistent with the observations and satisfy certain desired global topological properties such as a given mean density, extending the approach by Gandy and Veraart (2017). Furthermore we propose a new fitness-based model within this framework. We provide a case study based on a data set consisting of 89 fully observed financial networks of credit default swap exposures. We reconstruct those networks based on only partial information using the newly proposed as well as existing methods. To assess the quality of the reconstruction, we use a wide range of criteria, including measures on how well the degree distribution can be captured and higher order measures of systemic risk. We find that the empirical Bayesian approach performs best

Compound poisson models for weighted networks with applications in finance

We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships be- tween the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks

Sensitivity of the Eisenberg-Noe clearing vector to individual interbank liabilities

We quantify the sensitivity of the Eisenberg-Noe clearing vector to estimation errors in the bilateral liabilities of a financial system in a stylized setting. The interbank liabilities matrix is a crucial input to the computation of the clearing vector. However, in practice central bankers and regulators must often estimate this matrix because complete information on bilateral liabilities is rarely available. As a result, the clearing vector may suffer from estimation errors in the liabilities matrix. We quantify the clearing vector's sensitivity to such estimation errors and show that its directional derivatives are, like the clearing vector itself, solutions of fixed point equations. We describe estimation errors utilizing a basis for the space of matrices representing permissible perturbations and derive analytical solutions to the maximal deviations of the Eisenberg-Noe clearing vector. This allows us to compute upper bounds for the worst case perturbations of the clearing vector in our simple setting. Moreover, we quantify the probability of observing clearing vector deviations of a certain magnitude, for uniformly or normally distributed errors in the relative liability matrix. Applying our methodology to a dataset of European banks, we find that perturbations to the relative liabilities can result in economically sizeable differences that could lead to an underestimation of the risk of contagion. Our results are a first step towards allowing regulators to quantify errors in their simulations.Comment: 37 page

Reconstructing firm-level interactions in the Dutch input–output network from production constraints

Recent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints

CAGIRE: a wide-field NIR imager for the COLIBRI 1.3 meter robotic telescope

The use of high energy transients such as Gamma Ray Bursts (GRBs) as probes of the distant universe relies on the close collaboration between space and ground facilities. In this context, the Sino-French mission SVOM has been designed to combine a space and a ground segment and to make the most of their synergy. On the ground, the 1.3 meter robotic telescope COLIBRI, jointly developed by France and Mexico, will quickly point the sources detected by the space hard X-ray imager ECLAIRs, in order to detect and localise their visible/NIR counterpart and alert large telescopes in minutes. COLIBRI is equipped with two visible cameras, called DDRAGO-blue and DDRAGO-red, and an infrared camera, called CAGIRE, designed for the study of high redshift GRBs candidates. Being a low-noise NIR camera mounted at the focus of an alt-azimutal robotic telescope imposes specific requirements on CAGIRE. We describe here the main characteristics of the camera: its optical, mechanical and electronics architecture, the ALFA detector, and the operation of the camera on the telescope. The instrument description is completed by three sections presenting the calibration strategy, an image simulator incorporating known detector effects, and the automatic reduction software for the ramps acquired by the detector. This paper aims at providing an overview of the instrument before its installation on the telescope.Comment: Accepted by Experimental Astronom

Financial stability challenges in EU candidate countries - Financial systems in the aftermath of the global crisis

This paper reviews financial stability challenges in the EU candidate countries: Croatia, the former Yugoslav Republic of Macedonia and Turkey. It follows a macro-prudential approach, emphasising systemic risks and the stability of financial systems as a whole. The paper recalls that the economies of all three countries experienced a recession in 2008-09 and shows how this slowed the rapid process of financial deepening that had been taking place since the beginning of the last decade. The deteriorating economic and financial conditions manifested themselves, first and foremost, through a marked deterioration in asset quality. These direct credit risks were compounded by the transformation of exchange and interest rate risks through a widespread use of foreign exchange-denominated or indexed loans and variable or adjustable interest rate loans. Moreover, funding and liquidity risks also materialised to some extent, although fully fledged bank runs were avoided, and none of the countries experienced a sharp reversal in external financing. Overall, the deterioration in asset quality has so far been managed well by the banking systems of the candidate countries, facilitated by large capital buffers, pro-active macro-prudential policies pursued by the authorities both before and during the crisis and the relative stability of exchange rates. Looking ahead, although uncertainties remain high regarding credit quality, the shock-absorbing capacities of the banking systems are fairly robust, as also evidenced by their relative resilience so far. Nevertheless, as the economic recovery sets in, the central banks should return to and possibly reinforce the implementation of measures to avoid a pro-cyclical build-up of credit asset) boom-bust cycles. Furthermore, given the relevance of foreign-owned banks in two of the three countries, a continued strengthening of home-host cooperation in the supervisory area will be crucial to avoid any kind of regulatory arbitrage. JEL Classification: E3, E52, E58banking sector, emerging markets, Europe, macro-prudential approach, vulnerability indicators