Modelling Deposit Insurance Scheme Losses in a Basel 2 Framework

Lisa, Riccardo De; Zedda, Stefano; Vallascas, Francesco; Campolongo, Francesca; Massimo, Marchesi

doi:10.2139/ssrn.1320613

Cited by 14 publications

(17 citation statements)

References 25 publications

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“…A different approach, somewhat standing between the previous ones, based on the capital at risk reported by banks in their financial statements, is provided by the Symbol model of De Lisa et al (2011). This approach inverts the Vasicek model Basel II formula (Vasicek, 1984) so to obtain, for each bank, the probability of default that corresponds to the amount of regulatory capital set aside for the coverage of financial risks.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating bank default with generalised extreme value regression models

Calabrese

Giudici

2015

Journal of the Operational Research Society

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The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic conditions are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of prediction effectiveness, the model based on extreme value theory outperforms classical logistic regression models.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating bank default with generalised extreme value regression models

Calabrese

Giudici

2015

Journal of the Operational Research Society

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“…Some authors put emphasis on the necessity to link the amount of funds to the DPS loss distribution. 31,32 Coverage ratio…”

Section: Financial Resourcesmentioning

confidence: 99%

The promptness of European Deposit Protection Schemes to face banking failures

2010

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“…SYMBOL is a statistical tool estimating losses deriving from bank defaults, explicitly linking Basel capital requirements to the other key tools of the banking safety net, i.e. Deposit Guarantee Schemes, and bank Resolution Funds (De Lisa et al 2011). This tool has been used by Commission Services to prepare various Impact Assessments of European Commission (EC) regulatory proposals to enhance financial stability and prevent future crises (Capital Requirement Directive Proposal, Bank Recovery and Resolution Directive and Financial Transactions Tax).…”

Section: Introductionmentioning

confidence: 99%

“…5 The choice of the 50 % correlation is based on (Sironi and Zazzara 2004). A discussion and a sensitivity check on this assumption can be found in (De Lisa et al 2011). where N is the normal probability function and N −1 (α i, j ) are correlated normal random shocks, and I O P D i is the average implied obligors probability of default estimated for each bank in step 1.…”

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confidence: 99%

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Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture

Muresano

Pagano

2015

Comput Econ

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Currently, multi-core system is a predominant architecture in the computational word. This gives new possibilities to speedup statistical and numerical simulations, but it also introduce many challenges we need to deal with. In order to improve the performance metrics, we need to consider different key points as: core communications, data locality, dependencies, memory size, etc. This paper describes a series of optimization steps done on the SYMBOL model meant to enhance its performance and scalability. SYMBOL is a micro-funded statistical tool which analyses the consequences of bank failures, taking into account the available safety nets, such as deposit guarantee schemes or resolution funds. However, this tool, in its original version, has some computational weakness, because its execution time grows considerably, when we request to run with large input data (e.g. large banking systems) or if we wish to scale up the value of the stopping criterium, i.e. the number of default scenarios to be considered. Our intention is to develop a tool (extendable to other model having similar characteristics) where a set of serial (e.g. deleting redundancies, loop enrolling, etc.) and parallel strategies (e.g. OpenMP, and GPU programming) come together to obtain shorter execution time and scalability. The tool uses automatic configuration to make the best use of available resources on the basis of the characteristics of the input datasets. Experimental results, done varying the size of the input dataset and the stopping criterium, show a considerable improvement one can obtain by using

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Modelling Deposit Insurance Scheme Losses in a Basel 2 Framework

Cited by 14 publications

References 25 publications

Estimating bank default with generalised extreme value regression models

Estimating bank default with generalised extreme value regression models

The promptness of European Deposit Protection Schemes to face banking failures

Adapting and Optimizing the Systemic Model of Banking Originated Losses (SYMBOL) Tool to the Multi-core Architecture

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