A simulation approach to distinguish risk contribution roles to systemic crises

The last financial crisis has shown that large banking crises pose a highly dangerous risk to both the real economy and public finances. Reducing that risk has become a priority for regulators and governments, but the debate on how to deal with it remains open. Contagion plays a key role: domino effects can turn a relatively small difficulty into a systemic crisis. It is thus important to assess how contagion spreads across banking systems and how to distinguish the two roles played by ‘lighters’ and ‘fuel’ in the crisis, i.e. which banks are likely to start financial contagion and which have a ‘passive’ role in just being driven to default by contagion. The aim of this paper is to propose a methodology for distinguishing the two roles, and for assessing their different contributions to systemic crises. For this purpose, we have adapted a Monte Carlo simulation-based approach for banking systems which models both correlation and contagion between banks. Selecting large crises in simulations, and finding which banks started each simulated crisis, allows us to distinguish ‘primary’ and ‘induced’ defaults and fragility, and to determine the contribution of individual banks to the triggering of systemic crises. The analysis has been tested on a sample of 83 Danish banks for 2010.JRC.G.1-Scientific Support to Financial Analysi

The Role of Contagion in Financial Crises: an Uncertainty Test on Interbank Patterns

The main lesson learned from the recent financial crisis is the crucial role of interconnectedness between banks as a factor that can push the effects of bank defaults to extreme levels. One bank in distress can compromise the ability to repay obligations of its creditor banks, thereby inducing a more general crisis that spreads from the banking system towards the real economy. Several empirical and theoretical studies have focused on the role of the interbank market in causing contagion in financial crises. In this regard, one frequent problem encountered in dealing with contagion risk in the banking system is that only data on interbank credits and debts aggregated at bank level are publicly available, whereas the whole matrix of interbank linkages would be needed in order to estimate systemic risk correctly. One common solution is to assume that banks maximise the dispersion of their interbank credits and debts, so that the interbank matrix can be approximated by its maximum entropy. This paper tests the influence of this hypothesis on simulations by verifying if variations in the structure of the interbank matrix lead to significant changes in the magnitude of contagion. In order to do this, an algorithm was developed that generates interbank matrices with higher concentration. Then a Monte Carlo simulation was run by making use of the SYMBOL model (SYstemic Model of Banking Originated Losses) jointly developed by the JRC, DG MARKT, and experts of banking regulation (see De Lisa et al., 2010). We than compared results obtained using the maximum entropy approximated matrix with those obtained from more concentrated matrices. Numerical experiments, performed on samples of banks from four European countries, highlight that concentration in interbank loans does affect results but that, when considering the probability distribution of losses, even significant changes in the interbank matrix do not deeply affect results.JRC.G.1-Scientific Support to Financial Analysi

JRC technical work supporting Commission second level legislation on risk based contributions to the (single) resolution fund

JRC supported the DG MARKT by developing quantitative analyses for the preparation of the second level legislation on bank contributions to be paid to the EU national Resolution Funds and to the Single Resolution Fund SRF for countries participating to the Banking Union. The present report summarizes all the extensive analyses on the calculation of banks contributions supporting the whole policy process. All analyses were based on a dataset that JRC built assembling individual bank unconsolidated balance sheet data, provided directly by the MS. JRC developed the technical details to measure the risk profile of each bank. Starting from a selection of balance sheet indicators, which account for the different aspects of each bank activity, the methodology aggregates them into a single composite risk indicator. The risk indicator is then combined with the bank size measure to compute the share of aggregated contribution each bank joining the fund would pay. JRC also investigated the decrease in contributions of applying a special treatment for the computation of the small banks’ contributions: these banks will not pay contributions based on their risk profile but will be instead lump-sum contributions, depending on their size only. JRC assessed the sensitivity of the distribution of contributions when changing some elements of the overall mechanism used to measure risk and compute contributions. Finally, following the discussion at the political level, JRC also assessed some technical issues related to the calculation of the contribution base and it tested the impact on banks contributions of different options for the phasing in of the single resolution fund.JRC.G.1-Financial and Economic Analysi

survival probabilities for hiv infected patients through semi markov processes

SUMMARY In this paper we apply a parametric semi-Markov process to model the dynamic evolution of HIV-1 infected patients. The seriousness of the infection is rendered by the CD4+ T-lymphocyte counts. For this purpose we introduce the main features of nonhomogeneous semi-Markov models. After determining the transition probabilities and the waiting time distributions in each state of the disease, we solve the evolution equations of the process in order to estimate the interval transition probabilities. These quantities appear to be of fundamental importance for clinical predictions. We also estimate the survival probabilities for HIV infected patients and compare them with respect to certain categories, such as gender, age group or type of antiretroviral therapy. Finally we attach a reward structure to the aforementioned semi-Markov processes in order to estimate clinical costs. For this purpose we generate random trajectories from the semi-Markov processes through Monte Carlo simulation. The proposed model is then applied to a large database provided by ISS (Istituto Superiore di Sanità, Rome, Italy), and all the quantities of interest are computed

Financial Activities Taxes and Banks' Systemic Risk

The recent financial crisis has highlighted the risks posed by individual banks to the entire banking system. Next to the issue of determining individual contributions to systemic risk, the question of additional taxes on the financial sector has been debated. This paper uses SYMBOL, a micro-simulation model of the banking system, to estimate these individual contributions and compares them to the potential individual tax liabilities of banks under the assumption of a Financial Activity Tax.JRC.G.1-Scientific Support to Financial Analysi

HIV-1 drug resistance in recently HIV-infected pregnant mother's naïve to antiretroviral therapy in Dodoma urban, Tanzania

HIV resistance affects virological response to therapy and efficacy of prophylaxis in mother-to-child-transmission. The study aims to assess the prevalence of HIV primary resistance in pregnant women naïve to antiretrovirals

Analysis of Banks’ Systemic Risk Contribution and Contagion Determinants through the Leave-one-out Approach

In this paper we develop a strong analysis of the systemic risk and contagion determinants, through the differential effects on the banking system of excluding one bank. The first raw test of comparing the riskiness of a sample of banks by some different risk measures gives some interesting results

Analysis of banks' systemic risk contribution and contagion determinants through the leave-one-out approach

In this paper we develop an in-depth analysis of the systemic risk and contagion determinants through the differential effects of excluding one bank on the banking system.The measure allows for splitting the contribution of individual banks into systemic risk as the sum of two components-the stand-alone bank risk and the contagion risk-and measuring the role of assets, riskiness, capitalization, and interconnectedness as determinants of each of the two components. Results show that the variables determining the stand-alone risk component are different from those determining the contagion risk component, so that a bank which is relatively safe with respect to stand-alone risk, can be an important contagion vehicle, or vice versa.Results also show that crisis severity significantly affects results, so that the severity of different crises results in different weights for the input variables and different contributions for the banks considered. These results add highly significant information for macroprudential regulation, not only from the cross-sectional point of view, but also with reference to the time dimension

The determinants of interbank contagion: do patterns matter?

The recent financial crisis highlighted that interconnectedness between banks has a crucial role, and can push the effects of bank defaults to extreme levels. The distress of one bank can compromise the solvency of its creditor banks, possibly inducing a more general crisis that can even deeply affect the real economy. Several studies have focused on the role of the interbank market in causing contagion in financial crises. As only data on interbank credits and debts aggregated at bank level are publicly available, whereas the whole matrix of interbank linkages would be needed in order to estimate systemic risk correctly, some approximation is needed. One common solution is to assume that banks maximize the dispersion of their interbank credits and debts, so that the interbank matrix is approximated by its maximum entropy realization. The aim of this paper is to test the influence of this approximation on simulations, and verifying if variations in the structure of the interbank matrix systematically change the magnitude of contagion. In order to do this, an algorithm was developed for generating interbank matrices with higher concentration, and, via a Monte Carlo simulation, a counterfactual test was realized comparing results obtained using the maximum entropy approximated matrix with those obtained from more concentrated matrices. Performing numerical experiments on samples of banks from four European countries, resulted in small changes in the point estimation, but variability and confidence interval for the estimates are deeply affected, in particular in banking systems when contagion effects are more important.JRC.G.1-Financial and Economic Analysi