Operational Risk Capital Charge Research Articles

Operational risk assessment of third-party payment platforms: a case study of China

Operational risk events have severely impacted the development of third-party payment (TPP) platforms, and have even led to a discussion on the operational risk capital charge settlement by relevant international regulators. However, prior studies have mostly focused on qualitative mechanism analysis, and have rarely examined quantitative risk assessment based on actual operational risk events. Therefore, this study attempts to assess the operational risk on TPP platforms in China by constructing a systematic framework incorporating database construction and risk modeling. First, the operational risk database that covers 202 events between Q1, 2014, and Q2, 2020 is constructed. Then, specific causes are clarified, and the characteristics are analyzed from both the trend and loss severity perspectives. Finally, the piecewise-defined severity distribution based-Loss Distribution Approach (PSD-LDA) with double truncation is utilized to assess the operational risk. Two main conclusions are drawn from the empirical analysis. First, legal risk and external fraud risk are the two main causes of operational risk. Second, the yearly Value at Risk and Expected Shortfall are 724.46 million yuan and 1081.98 million yuan under the 99.9% significance level, respectively. Our results are beneficial for both TPP platform operators and regulators in managing and controlling operational risk.

Risk shifting and regulatory arbitrage: Evidence from operational risk

We document a robust negative relation between operational risk exposure and bank capital levels for a sample of large U.S. banks under the Basel I Capital Accords. The results are consistent with the notion that capital-constrained banks increased operational risk exposure at the time when Basel I regulations did not require an explicit capital charge for operational risk. More broadly, our results show new channel by which financial regulations incentivize banks to shift their risk taking to less regulated risk areas. We focus on the case of operational risk because it went from a largely unregulated risk type to a major risk that accounts for about 25% of large U.S. banks’ risk-weighted assets.

Operational Risk Management and the Case of Albania

Starting 1999, when operational risk was instroduced for the first time as part of pillar 1 minimum regulatory capital charge, supervisors and the banking industry recognized the importance of such risk in evaluating the risk profiles of financial institutions. The increasing use of automated technology, the growth of e-commerce and the expansion of activity ect. , create increased operational risk, and expose the institution to possible losses. Such risk has been introduced in the regulatory framework of Bank of Albania in 2011 and since then, there have been positive developments in the consideration of this risk from the supervisory point of view. The regulation included qualitative criteria for the identification and monitoring of operational risk, whereas the quantitative measurement of capital charge for operational risk has been introduced through the implementation of capital adequacy ratio regulation based in Basel 2.

Operational risk: impact assessment of the revised standardized approach on Indian banks

The Basel guidelines on operational risk reinforce the relevance of so-called residual risk and outline procedural changes to demonstrate and reinforce policy effectiveness. Banks have been encouraged to move along the spectrum of available approaches (i.e., the basic indicator approach, standardized approach and advanced methods approach) for capital calculation as they develop more sophisticated operational risk measurement frameworks and practices. With the proposed new revised standardized approach for operational risk capital charge calculation, it might be of interest to see the change in capital estimations of Indian banks as per the revised framework. If adopted, this framework will not pose a huge challenge in terms of capital requirements for smaller banks. Larger banks may show an increase in capital charges as per the revised framework; however, the estimated impact on Tier 1 capital will be minimal.

A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion

It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.

Operational Risk Aggregation across Business Lines Based on Frequency Dependence and Loss Dependence

In loss distribution approach (LDA), the most popular approach in operational risk modeling, frequency dependence and loss distribution dependence across business lines are two dependences which banks should consider. In practice, mainly for simplicity, many banks only model frequency dependence although they think that the impact of frequency dependence is insignificant. In this study, two approaches, respectively, models frequency dependence and loss distribution dependence, are introduced. Both approaches are modeled by copula function, which is capable of capturing nonlinear correlation. Based on the most comprehensive operational risk dataset of Chinese banking as far as we know, the operational risk capital charge of the overall Chinese banking is calculated by the two approaches. The results show that there is an obvious distinction between the capital calculated by modeling frequency dependence and the capital calculated by modeling loss dependence. The approach with very limited attention exactly yields a much larger capital result. So it is advised in this paper that banks should not just rely on the approach to modeling frequency dependence for it is natural and easy to deal with. A safer and more effective way for banks is to comprehensively take the results of the two kinds of approach into consideration.

Modeling Truncated Loss Data of Operational Risk in E-banking

Operational risk is an important risk component for financial institutions, especially in E- banking. Large amount of capital that are assigned to decrease this risk are evidence to this subject. One of the most important factors for modeling operational risk to estimate capital charge is loss data collections of banks. But sometimes for reasons like decreasing the costs, banks save only the losses above some determined thresholds at their database. For achieving accurate capital charge, this threshold should be considered in determining capital charge. This paper focuses on modeling truncated loss data above some given threshold. We discuss several statistical methods for modeling truncated data, and suggest the best one for modeling truncated loss data. We have tested our suggested model for some operational loss data samples. Our results indicate that our approach can be useful for increasing accuracy of estimating operational risk capital charge in E- banking.

Measuring the capital charge for operational risk of a bank with the large deviation approach

In this paper, the large deviation approach for computing the capital charge for operational risk of a bank is explored. Firstly, the negatively-associated structure is utilized to measure the dependence between distinct operational loss cells. Secondly, the lower and upper bounds of the tail distribution function of total aggregated loss processes are determined. In addition, first order approximations using a value-at-risk measure are derived. Finally, an important example calculating the capital charge for operational risk under the class of a heavy-tailed distribution is provided.

The use of key risk indicators by banks as an operational risk management tool: A South African perspective

The use of key risk indicators as a management tool is one of the requirements for the calculation of a bank’s operational risk capital charge. This article provides insight into the use of key risk indicators as an operational risk management tool by South African banks and indicates their level of preparedness to comply with the criteria. The results of a questionnaire aimed at junior and middle management indicated that banks are not suitably prepared to implement a key risk indicator management process and have a general lack of understanding of the underlying theory and concept of the criteria to use key risk indicators. The advantages of using key risk indicators are not fully exploited and more benefits can be realised by raising awareness in this regard.

Calculating Operational Risk Capital Charges for Indonesian Rural Banks

In January 2001, the Basel Committee on Banking Supervision published a proposal for a new capital framework, the “New Basel Capital Accord (Basel 11)” thus replacing Basel 1. One of the major motivations in the proposal is the introduction of explicit capital charge for operational risks in the business activities of banks. The objective of this paper is to estimate operational risk capital charge using historical data for 77 rural banks in Indonesia for a three-year period, 2006 to 2008. This study uses three approaches: (i) Basic Indicator Approach (BIA), (ii) Standardized Approach (SA) and (iii) Alternative Standardized Approach (ASA). We found that the average capital charge required to cover operational risk is IDR 154 million (1.5% of asset). When the calculation is conducted using the SA method, we found, on average a requirement of IDR 123 million (1.23% of asset). When the calculation is conducted using the Alternative Standardized Approach (ASA), the capital required was IDR 43 million (0.43% of asset). The results provide evidence that banks using more advance model require less capital charge.

Operational risk in Islamic banks: examination of issues

PurposeThe purpose of this paper is to assess key issues in measurement and management of operational risk in Malaysian Islamic banks.Design/methodology/approachDescriptive, analytical, and comparative analyses are used to discuss the issues of operational risk in Islamic bank through the implications associated with the Islamic banks' operational risk as well as the implications on risk measurement, risk management, and capital adequacy.FindingsDiscussion on operational risk in Islamic banks is significant and becoming more complicated compared with conventional banking because of the unique contractual features and general legal environment. While basic Basel II core principles of effective banking supervision apply equally well and ideally suit the Islamic banking institutions, risk measurement, and risk management practices still need specific adaptations to Islamic banks' operational characteristics. These particularities highlight the unique characteristics of Islamic banks and raise serious concerns regarding the applicability of the Basel II methodology for Islamic banks.Research limitations/implicationsThis study has important implications for the understanding of operational risk, particularly the specific issues of the Islamic banks' operational risk that arise from the different nature of the financing and investment activities of the banks. With regard to measuring operational risk capital charge, the banks have to choose the right and effective method to ensure the operational risk capital charge will be more in line with the banks' actual risk profile and thus will provide the adequate capital and an improved buffer once the losses are announced.Originality/valueThe paper will fill the gap to the existing literature of operational risk in banking institutions especially Islamic banks, by showing the needs of specific adaption of operational risk measurement and risk management practices due to the nature of Islamic banks.

Conceptualisation Of Operational Risk Management Models

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: "Times New Roman","serif"; font-size: 10pt;" lang="EN-AU">Operational risk is evolving as a specialist field of risk management that must be practiced within all organisations, but currently has a particular relevance to banks. The Basel Committee on Banking Supervision has circulated a consultative paper which, if adopted by nation-state bank supervisors, will impose an operational risk capital charge on banks as part of a new Capital Accord. The definition of operational risk is wide-ranging and creates some unique issues related to the development of appropriate risk management models. This paper conceptualises two distinct operational risk management models; being a predictive model that will result in a known outcome upon its implementation, and a pre-emptive operational risk management model which prepares an organisation in the event that a future risk occurrence results in a disruption to critical business operations.</span></p>

Preparedness of banks to be compliant with the criteria for the advanced measurement approach: A South African perspective

The New Basel Accord proposed qualitative and quantitative criteria for banks to use the Advanced Measurement Approach to calculate a capital charge for operational risk. The question now is how prepared are banks in South Africa? This article provides insight into relevant criteria, indicating the level of preparedness of banks for the Advanced Measurement Approach. An analysis based on results of a questionnaire, aimed at junior and middle management levels, indicated that banks are more compliant with qualitative than quantitative criteria. It also indicated a general lack of understanding of certain criteria. Should a bank want to implement the Advanced Measurement Approach, it is imperative that criteria be clear and that all role-players be knowledgeable about relevant systems and processes.

Implementing loss distribution approach for operational risk

AbstractIn 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.

Loss Distribution Approach for Quantifying the Operational Risk Capital Charge

Advanced Measurement Approach (AMA) has been addressed by the Basel Committee in document International Convergence of Capital Measurement and Capital Standards. Financial institutions must meet certain criteria to obtain regulatory approval. This paper proposes a logical structure to quantify the operational risk capital charge with a Loss Distribution Approach (LDA), including techniques and mathematical proofs for better aid.

Estimating Operational Risk Capital Charges for Indonesian Rural Banks

In January 2001, the Basel Committee on Banking Supervision published a proposal for a new capital framework, the “New Basel Capital Accord (Basel 11)” thus replacing Basel 1. One of the major motivations in the proposal is the introduction of explicit capital charge for operational risks in the business activities of banks. The objective of this paper is to estimate operational risk capital charge using historical data for 77 rural banks in Indonesia for a three-year period, 2006 to 2008. The study uses three approaches: (i) Basic Indicator Approach (BIA), (ii) Standardized Approach (SA) and (iii) Alternative Standardized Approach (ASA). We found that the average capital charge required to cover operational risk is IDR 154 million (1.5% of asset). When the calculation is conducted using the SA method, we found, on average a requirement of IDR 123 million (1.23% of asset). When the calculation is conducted using the Alternative Standardized Approach (ASA), the capital required was IDR 43 million (0.43% of asset).

Who 'Owns' Operational Risk?

The increasing complexity of banking business has been responsible for the growing Operational risk events. Operational risk came to limelight during the Basel II negotiations on Capital charge. The nature of Operational risk is complex and dynamic. Unlike credit and market risk, operational risk is largely internal to banks, difficult to assess and has the potential to wipe out the very existence of the organization. It is proven without doubt that operational risk management improves the quality and stability of earnings, thereby enhancing the competitive position of the bank and facilitating its long term survival. For effective management of risk, an owner should be identified with each risk. For Operational risk the issue is more complex. One possible answer can be that the business lines own it, in which case ownership of Operational risk is aligned with the profit centre and the risk takers. In the standardised approach of Operational risk capital charge, Basel Committee on Banking Supervision used the mapping principle based on business line, which support the assumption that the business lines own operational risk and are responsible for day to day management. The present paper examines the efficacy of the proposed ownership of Operational risk.

The dynamics of operational loss clustering

This paper investigates the characteristics of the operational loss data formation mechanism that takes place between the date of discovery of a new operational risk event and the final settlement date on which all losses are materialized. The first loss that characterizes the initial impact of a new operational risk event frequently triggers a sequence of related losses. Then, losses generated by the same event are not independent and follow a predictable scheme and the frequency of secondary losses is not homogeneous: both are functions of the initial loss amount and time. We model the arrival intensity and loss severities with a shot-noise stochastic process and derive its key properties. We then discuss implications of our model for the estimation of the regulatory capital charge for operational risk. In an empirical analysis, we find strong evidence of a shot-noise behavior in operational losses using the data of a major US commercial bank.

Estimation of operational risk capital charge under parameter uncertainty

Many banks adopt the Loss Distribution Approach to quantify the operational risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution parameters. The uncertainty of the parameter estimates is typically ignored. One of the unpleasant consequences for the banks accounting for parameter uncertainty is an increase in the capital requirement. This paper demonstrates how the parameter uncertainty can be taken into account using a Bayesian framework that also allows for incorporation of expert opinions and external data into the estimation procedure.

Challenges facing banks in emerging countries to be Basel compliant for operational risk

The New Basel Accord identified various requirements for an effective operational risk management framework. Most central banks and regulators adopted these requirements for their own banking environments. However, there are many challenges facing these banks to ensure the effective incorporation of such a framework. An end-result of establishing an operational risk management framework is to calculate and allocate a realistic capital charge for operational risk. To achieve this, various principles and methodologies must be embedded that will ensure a practical approach to operational risk management. This paper aims to identify certain critical issues and challenges for banks of emerging countries to consider when developing an operational risk management framework in order to comply with the Basel requirements.