Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method
Abstract
:1. Introduction
2. Literature Overview
2.1. Digital Banking Transaction
2.2. Digital Banking Transaction Risks
2.3. Operational Risk
2.4. Measurement of Operational Risk Based on the Basel Standard
3. Materials and Methods
3.1. Materials
3.2. Methods
3.2.1. Maximum Entropy Bootstrapping (MEBoot)
3.2.2. Threshold
3.2.3. Extreme Value Theory (EVT)
3.2.4. Peaks-Over-Threshold (POT)
3.2.5. Generalized Pareto Distribution (GPD)
3.2.6. Extreme Value-at-Risk (EVaR) Method
4. Results
4.1. Determination of Threshold and Extreme Data through the MEBoot Process
4.2. Goodness-of-Fit of Extreme Data to the GPD
4.3. Estimation of the GPD Parameter
4.4. Estimation of Maximum Potential Losses
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Titles | Methods | Risks (Variables) |
---|---|---|---|
Gilli and Këllezi (2006) | An Application of Extreme Value Theory for Measuring Financial Risk | Extreme value theory, value-at-risk, expected shortfall | Market risk, financial series, daily returns of some financial portfolios |
Esterhuysen et al. (2008) | Calculating Operational Value-at-Risk in a Retail Bank | Operational value-at-risk, advanced measurement approach, standardized approach | Operational losses data in the retail bank, gross income, net interest income |
Yao et al. (2013) | CVaR Measurement and Operational Risk Management in Commercial Banks According to the Peak Value Method of the Extreme Value Theory | Conditional value-at-risk, peak value method, extreme value theory | Operational risks, losses data of commercial banks |
van Schalkwyk and Witbooi (2017) | A Model for Bank Reserves Versus Treasuries under Basel III | Portfolio diversification, stochastic optimal control | Deposit risk, cumulative cost, net cash flows in a bank |
This work | The Estimation of Maximum Potential Losses of Digital Banking Transactions Risks Using the Extreme Value-at-Risks Method | Extreme value-at-risk, portfolio approach | Operational risks, digital banking transactions risks |
Risk Type | Resample | Lots of Data | Lots of Extreme Data | Threshold (IDR) |
---|---|---|---|---|
Downtime | 10 | 1230 | 123 | IDR81,080,836,365 |
Timeout | 10 | 1230 | 123 | IDR64,806,050,343 |
(a) | |||||
Kolmogorov–Smirnov Test | |||||
Sample Size | 123 | ||||
Statistic | 0.0599 | ||||
p-Value | 0.74642 | ||||
0.2 | 0.1 | 0.05 | 0.02 | 0.01 | |
Critical Value | 0.09675 | 0.11207 | 0.12245 | 0.13687 | 0.14688 |
Reject? | No | No | No | No | No |
(b) | |||||
Kolmogorov–Smirnov Test | |||||
Sample Size | 123 | ||||
Statistic | 0.09628 | ||||
p-Value | 0.19151 | ||||
0.2 | 0.1 | 0.05 | 0.02 | 0.01 | |
Critical Value | 0.09675 | 0.11207 | 0.12245 | 0.13687 | 0.14688 |
Reject? | No | No | No | No | No |
Descriptive Statistics | (Timeout Risks) | (Downtime Risks) |
---|---|---|
Data | 123 | 123 |
Mean | 118,715,100,522.2 | 116,326,654,020.561 |
Standard Deviation | 54,110,542,295.94 | 43,314,723,866.9 |
Sample Variance | 2.927950787 × 1025 | 1.876165303 × 1021 |
Kurtosis | 0.263336436495423 | 3.67055481773892 |
Skewness | 1.16545766227375 | 2.01686434554239 |
Minimum | 65,183,178,591.0188 | 81,410,403,286.1475 |
Maximum | 286,932,960,062.233 | 278,181,957,858.857 |
Sum | 1,4601,957,364,231 | 14,308,178,444,528.9 |
Parameter | Timeout Risk | Downtime Risk |
---|---|---|
Shape Parameter | −0.4513410383432 | −0.367209611440187 |
Scale Parameter | 118,715,100,522.203 | 116,326,654,020.561 |
Risk | EVaR | Weight of Risks |
---|---|---|
Timeout Risk | IDR135,465,044,269 | 0.4708 |
Downtime Risk | IDR152,268,681,535 | 0.5292 |
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Saputra, M.P.A.; Sukono; Chaerani, D. Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method. Risks 2022, 10, 10. https://doi.org/10.3390/risks10010010
Saputra MPA, Sukono, Chaerani D. Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method. Risks. 2022; 10(1):10. https://doi.org/10.3390/risks10010010
Chicago/Turabian StyleSaputra, Moch Panji Agung, Sukono, and Diah Chaerani. 2022. "Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method" Risks 10, no. 1: 10. https://doi.org/10.3390/risks10010010
APA StyleSaputra, M. P. A., Sukono, & Chaerani, D. (2022). Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method. Risks, 10(1), 10. https://doi.org/10.3390/risks10010010