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MANAGING OPERATIONAL RISK IN BANKS  [PDF]
Victoria STANCIU
Scientific Annals of the Alexandru Ioan Cuza University of Iasi : Economic Sciences Series , 2010,
Abstract: Recent years have emphasized focus on risk management, and it became clear that there is an urgent need for a robust framework to effectively manage risk. The financial and economic crisis showed the importance of a strong risk management process and forced banks to take a critical look at how they manage risks. Romanian banking system has known significant changes determined by the implementation of Basel II requirements. These requirements determined an important effort of the banks to improve their risk management process.Operational risk is considered a significant risk and has an important impact on banks activity and results. Now, there is a clear effort of the banks for applying more advanced methods on operational risk so that their control and management to be improved. The present paper presents the specificity of the operational risk management and the author’s solution for the operational risk management in banks.
Operational risk and e-banking
T?nase, R. D.,?erbu, R.
Bulletin of the Transilvania University of Brasov. Series V : Economic Sciences , 2010,
Abstract: Banking involves a variety of risks. Under Basel II, the main risks are the monitored credit risk, market risk and operational risk. Frequently, operational risks are underestimated, considering that they would not affect the optimal activity of a bank. However, past experience of some credit institutions have shown that operational risk is an important cause of financial losses in the banking sector. Operational risk is generated by a complex of factors that manifests primarily as a result of direct customer interaction with the credit institution. In this context, the provision of e-banking services reduces direct contact with bank customers and thus reduces potential losses arising from operational risk. In sum, we consider it necessary to be aware of the link between operational risk and e-banking services promoted by banks and of the importance of this connection especially in a financial environment affected by the financial crisis.
INSURANCE OF BANKING OPERATIONAL RISK  [PDF]
Bente Corneliu Cristian
Annals of the University of Oradea : Economic Science , 2010,
Abstract: The current financial crisis is not a singular event in the history of crisis episodes. The essential difference between past episodes of financial turmoil and the actual crisis is the unprecedented severity, the pace of contagion and its global size. Financial markets have been seriously disturbed, threatening the robustness of financial institutions and their ability to meet current needs to properly manage the risk. One such risk is operational risk, which has become an important source of loss for credit institutions. In this context, the main purpose of this study is to present the best techniques and methods of managing this risk, less addressed problem in the literature from our country.
The Management of Operational Risk Specific to Non-banking Financial Institutions in the Context of Actual Financial Crisis
Nicolae DARDAC,Petronel CHIRIAC
Theoretical and Applied Economics , 2010,
Abstract: The current financial crisis is not a singular event in the history of crisis episodes. The essential difference between past episodes of financial turmoil and the actual crisis is the unprecedented severity, the pace of contagion and its global size. Financial markets have been seriously disturbed, threatening the robustness of financial institutions and their ability to meet current needs to properly manage the risk. One such risk is operational risk, which has become an important source of loss not only for credit institutions but, especially, for non-banking financial institutions (NFI). In this context, the main purpose of this study is to present the best techniques and methods of managing this risk, less addressed problem in the literature from our country.
Operational Risk Modelling in Insurance and Banking  [PDF]
Ognjen Vukovic
Journal of Financial Risk Management (JFRM) , 2015, DOI: 10.4236/jfrm.2015.43010
Abstract: The author of the presented paper is trying to develop and implement the model that can mimic the state of the art models of operational risk in insurance. It implements generalized Pareto distribution and Monte Carlo simulation and tries to mimic and construct operational risk models in insurance. At the same time, it compares lognormal, Weibull and loglogistic distribution and their application in insurance industry. It is known that operational risk models in insurance are characterized by extreme tails, therefore the following analysis should be conducted: the body of distribution should be analyzed separately from the tail of the distribution. Afterwards the convolution method can be used to put together the annual loss distribution by combining the body and tail of the distribution. Monte Carlo method of convolution is utilized. Loss frequency in operational risk in insurance and overall loss distribution based on copula function, in that manner using student-t copula and Monte Carlo method are analysed. The aforementioned approach represents another aspect of observing operational risk models in insurance. This paper introduces: 1) Tools needed for operational risk models; 2) Application of R code in operational risk modeling;3) Distributions used in operational risk models, specializing in insurance; 4) Construction of operational risk models.
OPERATIONAL BANKING RISK MANAGEMENT – RESEARCH PERFORMED AT THE ROMANIAN COMMERCIAL BANK  [PDF]
Matis Eugenia - Ana
Annals of the University of Oradea : Economic Science , 2009,
Abstract: The main objective of this research is to provide a detailed perspective of the operational risk in the case of commercial bank. The research is based on the main regulations and procedures in the field of the operational banking risk, and it presents the
Functional Correlation Approach to Operational Risk in Banking Organizations  [PDF]
Reimer Kuehn,Peter Neu
Physics , 2002, DOI: 10.1016/S0378-4371(02)01822-8
Abstract: A Value-at-Risk based model is proposed to compute the adequate equity capital necessary to cover potential losses due to operational risks, such as human and system process failures, in banking organizations. Exploring the analogy to a lattice gas model from physics, correlations between sequential failures are modeled by as functionally defined, heterogeneous couplings between mutually supportive processes. In contrast to traditional risk models for market and credit risk, where correlations are described by the covariance of Gaussian processes, the dynamics of the model shows collective phenomena such as bursts and avalanches of process failures.
Opening discussion on banking sector risk exposures and vulnerabilities from virtual currencies: An operational risk perspective  [PDF]
Gareth W. Peters,Ariane Chapelle,Efstathios Panayi
Computer Science , 2014,
Abstract: We develop the first basic Operational Risk perspective on key risk management issues associated with the development of new forms of electronic currency in the real economy. In particular, we focus on understanding the development of new risks types and the evolution of current risk types as new components of financial institutions arise to cater for an increasing demand for electronic money, micro-payment systems, Virtual money and cryptographic (Crypto) currencies. In particular, this paper proposes a framework of risk identification and assessment applied to Virtual and Crypto currencies from a banking regulation perspective. In doing so, it addresses the topical issues of understanding important key Operational Risk vulnerabilities and exposure risk drivers under the framework of the Basel II/III banking regulation, specifically associated with Virtual and Crypto currencies. This is critical to consider should such alternative currencies continue to grow in utilisation to the point that they enter into the banking sector, through commercial banks and financial institutions who are beginning to contemplate their recognition in terms of deposits, transactions and exchangeability for fiat currencies. We highlight how some of the features of Virtual and Crypto currencies are important drivers of Operational Risk, posing both management and regulatory challenges that must start to be considered and addressed both by regulators, central banks and security exchanges. In this paper we focus purely on the Operational Risk perspective of banks operating in an environment where such electronic Virtual currencies are available. Some aspects of this discussion are directly relevant now, whilst others can be understood as discussions to raise awareness of issues in Operational Risk that will arise as Virtual currency start to interact more widely in the real economy.
An Application of Bayesian Inference on the Modeling and Estimation of Operational Risk Using Banking Loss Data  [PDF]
Kashfia N. Rahman, Dennis A. Black, Gary C. McDonald
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.56082
Abstract:

Bayesian inference method has been presented in this paper for the modeling of operational risk. Bank internal and external data are divided into defined loss cells and then fitted into probability distributions. The distribution parameters and their uncertainties are estimated from posterior distributions derived using the Bayesian inference. Loss frequency is fitted into Poisson distributions. While the Poisson parameters, in a similar way, are defined by a posterior distribution developed using Bayesian inference. Bank operation loss typically has some low frequency but high magnitude loss data. These heavy tail low frequency loss data are divided into several buckets where the bucket frequencies are defined by the experts. A probability distribution, as defined by the internal and external data, is used for these data. A Poisson distribution is used for the bucket frequencies. However instead of using any distribution of the Poisson parameters, point estimations are used. Monte Carlo simulation is then carried out to calculate the capital charge of the in- ternal as well as the heavy tail high profile low frequency losses. The output of the Monte Carlo simulation defines the capital requirement that has to be allocated to cover potential operational risk losses for the next year.

The necessity of operational risk management and quantification  [PDF]
Barbu Teodora Cristina,Olteanu (Puiu) Ana Cornelia,Radu Alina Nicoleta
Annals of the University of Oradea : Economic Science , 2008,
Abstract: Beginning with the fact that performant strategies of the financial institutions have programmes and management procedures for the banking risks, which have as main objective to minimize the probability of risk generation and the bank’s potential exposure, this paper wants to present the operational risk management and quantification methods. Also it presents the modality of minimum capital requirement for the operational risk. Therefore, the first part presents the conceptual approach of the operational risks through the point of view of the financial institutions exposed to this type of risk. The second part describes the management and evaluation methods for the operational risk. The final part of this article presents the approach assumed by a financial institution with a precise purpose: the quantification of the minimum capital requirements of the operational risk.
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