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Operational Risk (operational + risk)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	68%

Selected Abstracts

THE CONTRIBUTION OF THIRD-PARTY INDICES IN ASSESSING GLOBAL OPERATIONAL RISKS;,

JOURNAL OF SUPPLY CHAIN MANAGEMENT, Issue 4 2010
KUNTAL BHATTACHARYYA
In the face of global uncertainties and a growing reliance on third-party indices to obtain a snapshot of a country's operational risks, we explore the related questions: How accurately do third-party indices capture a country's operational risk, and how does the operational risk of the country, in turn, affect the volume of its import and export supply chains? We examine these questions by empirically investigating 81 member countries of the World Trade Organization (WTO) using archival data collected from UN agencies, independent think tanks, the WTO, and the Economist Intelligence Unit. We use seven third-party indices to gauge a country's internal environment and map those indices to corresponding country-specific operational risks to further understand the consequent effects of those operational risks on trading volume. Results provide strong evidence for the use of certain third-party indices in assessing operational risk. In addition, operational risks are found to negatively affect the volume of import and export supply chains, albeit in varying degrees. [source]

Risk Mapping and Key Risk Indicators in Operational Risk Management

ECONOMIC NOTES, Issue 2 2005
Sergio Scandizzo
In this article I describe a methodology for the mapping of Operational Risk with the objective of identifying the risks inherent in the different steps of a business process, selecting a set of variables providing an estimate for the likelihood and the severity of operational risk (Key Risk Indicators , KRIs) and designing the most appropriate control activities. I then present two examples of how the methodology described can be applied to map risks and of how a set of relevant KRIs can be identified in the front office of a trading business and in the back office of a lending business. Finally, I discuss how the information conveyed by the KRIs can be organised and summarised in order to provide a comprehensive look at the risk profile of the various business lines. The structured presentation of KRIs covering the business processes of a bank is what we call an Operational Risk Scorecard. [source]

Operational Risk Measurement in Banking Institutions and Investment Firms: New European Evidences

FINANCIAL MARKETS, INSTITUTIONS & INSTRUMENTS, Issue 4 2008
Enrique Bonsón
The banking/investment sector must deal with a new variable, Operational Risk, for explaining various recent crises and bankruptcies. Operational Risk, which can be defined briefly as the risk generated by possible failures of a entity's Information Systems (IS), must be measured, covered, mitigated and managed by applying a series of methodologies, each of which assumes that the IS of the bank operates at a certain Stage of Sophistication. The present study proposes a scheme of evolution that details the stages of enhancement in the sophistication of their IS that banking entities may implement, so as to be capable of capturing, mitigating and managing Operational Risk. Using econometric methods, we create a proxy variable to capture the IS Sophistication of each entity. Then, the factor of entity size has been analyzed, and the country effect is explored. Additionally, the importance of intangible assets is weighted, among others entity aspects. The entity size has been revealed as the variable with most influence on the plans formulated in this respect by European entities, against other variables also considered in the present study, such as the country effect or the importance of intangible assets. The work shows how IS decisions referring to Operational Risk management are very influenced by size. It could introduce competition differences in the European banking system. [source]

Sub-Optimality of Income Statement-Based Methods for Measuring Operational Risk under Basel II: Empirical Evidence from Spanish Banks

FINANCIAL MARKETS, INSTITUTIONS & INSTRUMENTS, Issue 4 2007
Enrique Bonsón
The New Basel Capital Accord (Basel II) was created with the intention of establishing a framework in which financial entities can manage their risks in a more detailed and efficient way. Within this general reform movement, Operational Risk emerges as a fundamental variable. OR can be managed by three alternative methods: the Basic Indicator Approach, Standard Approach and Advanced Measurement Approach. The choice of which method to adopt has become of supreme interest for senior banking managers. This study analyzes the exactitude of the underlying implicit hypotheses that support each method, distinguishing between income statement based methods and the management accounting based method. In the present study the non-optimum character of the two Income Statement-based methods is empirically confirmed, in the light of the data provided by Spanish financial entities. [source]

Risk Mapping and Key Risk Indicators in Operational Risk Management

THE CONTRIBUTION OF THIRD-PARTY INDICES IN ASSESSING GLOBAL OPERATIONAL RISKS;,

Modelling Operational Losses: A Bayesian Approach

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2004
Paolo Giudici
Abstract The exposure of banks to operational risk has increased in recent years. The Basel Committee on Banking Supervision (known as Basel II) has established a capital charge to cover operational risks other than credit and market risk. According to the advanced methods defined in ,The New Basel Capital Accord' to quantify the capital charge, in this paper we present an advanced measurement approach based on a Bayesian network model that estimates an internal measure of risk of the bank. One of the main problems faced when measuring the operational risk is the scarcity of loss data. The methodology proposed solves this critical point because it allows a coherent integration, via Bayes' theorem, of different sources of information, such as internal and external data, and the opinions of ,experts' (process owners) about the frequency and the severity of each loss event. Furthermore, the model corrects the losses distribution by considering the eventual relations between different nodes of the network that represent the losses of each combination of business line/event type/bank/process and the effectiveness of the corresponding internal and external controls. The operational risk capital charge is quantified by multiplying the value at risk (VaR) per event, a percentile of the losses distribution determined, by an estimate of the number of losses that may occur in a given period. Furthermore, it becomes possible to monitor the effectiveness of the internal and external system controls in place at the bank. The methodology we present has been experimented as a pilot project in one of the most important Italian banking groups, Monte dei Paschi di Siena. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Implementing loss distribution approach for operational risk

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2010
Pavel V. ShevchenkoArticle first published online: 21 OCT 200
Abstract In order to quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the loss distribution approach. There are many modeling issues that should be resolved to use this approach in practice. In this paper we review the quantitative methods suggested in the literature for the implementation of the approach. In particular, the use of Bayesian inference that allows one to take expert judgement and parameter uncertainty into account, modeling dependence, and inclusion of insurance are discussed. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Continuous-time stochastic modelling of capital adequacy ratios for banks

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2006
Casper H. Fouche
Abstract Regulation related to capital requirements is an important issue in the banking sector. In this regard, one of the indices used to measure how susceptible a bank is to failure, is the capital adequacy ratio (CAR). We consider two types of such ratios, viz. non-risk-based (NRBCARs) and risk-based (RBCARs) CARs. According to the US Federal Deposit Insurance Corporation (FDIC), we can further categorize NRBCARs into leverage and equity capital ratios and RBCARs into Basel II and Tier 1 ratios. In general, these indices are calculated by dividing a measure of bank capital by an indicator of the level of bank risk. Our primary objective is to construct continuous-time stochastic models for the dynamics of each of the aforementioned ratios with the main achievement being the modelling of the Basel II capital adequacy ratio (Basel II CAR). This ratio is obtained by dividing the bank's eligible regulatory capital (ERC) by its risk-weighted assets (RWAs) from credit, market and operational risk. Mainly, our discussions conform to the qualitative and quantitative standards prescribed by the Basel II Capital Accord. Also, we find that our models are consistent with data from FDIC-insured institutions. Finally, we demonstrate how our main results may be applied in the banking sector. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Evaluating extreme risks in invasion ecology: learning from banking compliance

DIVERSITY AND DISTRIBUTIONS, Issue 4 2008
James Franklin
ABSTRACT Increasing international trade has exacerbated the risks of ecological damage due to invasive pests and diseases. For extreme events such as invasions of damaging exotic species or natural catastrophes, there are no or very few directly relevant data, so expert opinion must be relied on heavily. Expert opinion must be as fully informed and calibrated as possible , by available data, by other experts, and by the reasoned opinions of stakeholders. We survey a number of quantitative and non-quantitative methods that have shown promise for improving extreme risk analysis, particularly for assessing the risks of invasive pests and pathogens associated with international trade. We describe the legally inspired regulatory regime for banks, where these methods have been brought to bear on extreme ,operational risks'. We argue that an ,advocacy model' similar to that used in the Basel II compliance regime for bank operational risks and to a lesser extent in biosecurity import risk analyses is ideal for permitting the diversity of relevant evidence about invasive species to be presented and soundly evaluated. We recommend that the process be enhanced in ways that enable invasion ecology to make more explicit use of the methods found successful in banking. [source]

THE CONTRIBUTION OF THIRD-PARTY INDICES IN ASSESSING GLOBAL OPERATIONAL RISKS;,

The profitability-risk tradeoff of just-in-time manufacturing technologies

MANAGERIAL AND DECISION ECONOMICS, Issue 5 2003
Jeffrey L. Callen
Qualitative survey studies and a recent quantitative study by Callen et al. (2000) indicate that JIT manufacturing is more profitable than conventional non-JIT manufacturing. This study tests the hypothesis that the excess profitability of JIT manufacturing just compensates for the additional operational risks of JIT technology relative to conventional manufacturing. An often-suggested alternative hypothesis is that JIT manufacturing dominates conventional manufacturing in reducing costs and increasing revenues and that risk is not an issue. The multivariate results unambiguously reject the hypothesis that excess JIT profits are compensation for additional risk. We find that profitability is inversely related to risk, especially for JIT plants. We also find that the JIT plants in our sample are more profitable than non-JIT plants even after adjusting for risk, consistent with the dominance argument. Copyright © 2003 John Wiley & Sons, Ltd. [source]