CVaR measurement and operational risk management in commercial banks according to the peak value method of extreme value theory

Yao, Fengge; Wen, Hongmei; Luan, Jiaqi

doi:10.1016/j.mcm.2012.07.013

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…They illustrated the differences of an internal risk control system in regulatory capital when using the advanced measurement approach (AMA) and the standardized approach (SA) by using examples of banking problems. Yao et al (2013) observed that operational risk management plays an important role in decision making for banks. The conditional value-at-risk (CVaR) model based on the peak value method from the EVT analysis is used to measure operational risk.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

Saputra

Chaerani

2022

Risks

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The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, it is worth implementing the EVT because of these operational risk characteristics. Yao et al (2013) realized the importance of EVT for measuring operational risk. However, the processing of some risks was carried out through data grouping.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

Saputra

Chaerani

2022

Risks

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“…Then illustrate the differences in regulatory capital when using the Advanced Measurement Approach (AMA) and the Standardized Approach (SA) by using examples of banking problems. Yao et al (2013) observe that operational risk management plays an important role in decision-making for banks. The Conditional Value-at-Risk (CVaR) model based on the peak value method from the extreme value theory is used to measure operational risk.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Reserve Funds for E-Banking Transactions using Operational Value-at-Risks

Halim¹,

Saputra²

2022

ijgor

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The “New Normal” state during the pandemic has made digital financial transactions important as an effort to reduce direct human interaction, to prevent the spread of the pandemic. The rate of financial transactions at banks has automatically increased, but in practice, several risks may occur about failed or incorrect digital transactions. Examples of digital transaction system risks are downtime and timeout services due to system failures, cyber-attacks, and system usage errors. These risks need attention from banking companies. One way to anticipate digital financial transaction failure happen is the readiness of a reserve fund that is used to cover the wrong amount of fund error in the bank's digital system. This research will discuss the estimation of operational reserve funds for digital banking financial transactions (e-banking) using the Operational Value-at-Risk (OpVaR) method, based on operational risk data for digital financial transactions to obtain the largest potential loss value from digital financial transaction activities at a bank. Based on calculations using the OpVaR method, it is known that the reserve fund required for the operational risk of digital financial transactions is IDR135,465,044,269.741. The results of this study show that the e-banking operational reserve fund is quite large due to the possibility of extreme losses. This provides a view to avoiding the worst risk of collapse due to an imbalance in the required reserve funds.

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“…Rosenberg used the normal Copula function and t-Copula function to calculate the aggregating risk of market risk, operational risk, and credit risk of commercial banks [5]. Yao et al utilized the peak value method and the conditional valueat-risk (CVaR) method, and adopted the Copula model to numerically simulate the expected and unexpected losses of operational risk in 14 commercial banks in China [6]. Hsu built extreme value Copula function, which solved the deviation problem caused by skewness and thick tails in the distribution which emerged when using the traditional method of calculating value at risk (VaR) [7].…”

Section: Introductionmentioning

confidence: 99%

Research on the Coupling Relationship between Market Risk and Credit Risk in Commercial Banks

Tian

Xin³

2018

MATEC Web Conf.

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With the complexity and diversity of business development, commercial banks gradually put more focus on how to improve the accuracy of risk measurement. In this essay, we first defined the basic market risk and credit risk indexes by the use of the financial data of the target bank. Then, we built the Copula Model through Monte Carlo simulation techniques. We finally built the Copula-VaR measurement model which revealed the relationship between the two types of risks.

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CVaR measurement and operational risk management in commercial banks according to the peak value method of extreme value theory

Cited by 17 publications

References 8 publications

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

Estimation of Reserve Funds for E-Banking Transactions using Operational Value-at-Risks

Research on the Coupling Relationship between Market Risk and Credit Risk in Commercial Banks

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