[ABSTRAK
Kelemahan dari pendekatan Basic Indicator Approach (BIA), Standardised Approach (SA), & Alternative Standardised Approach (ASA) terutama dari penggunaan gross income yang digunakan sebagai proxy untuk eksposur risiko operasional, kemudian tidak memperhitungkan fakta bahwa eksposur risiko operasional meningkat dengan ukuran bank secara non-linear. Penulis mencoba menganalisa pendekatan New Standardised Approach (NSA) yang baru saja diperkenalkan oleh BCBS291 (Oktober 2014) dengan dua strategi yang terdiri dari penyempurnaan perhitungan SA dengan menggunakan business indicators (BI) sebagai proxy perhitungan modal risiko operasional dan meningkatkan kalibrasi dari koefisien perhitungan modal risiko operasional berdasarkan range gross income tanpa melihat lagi faktor β (beta) dari 8 (delapan) business lines. BI menggunakan 3 (tiga) makro komponen, yaitu interest component, services component, dan financial. Pendekatan NSA terlihat cukup konservatif didalam mengalokasikan Operational Capital at Risk (OpCaR) dengan nilai pencadangan yang lebih besar dibandingkan dengan BIA & SA. Kemudian untuk melihat efisiensi alokasi OpCaR dalam penelitian ini dianalisa pendekatan AMA dengan metode Actuarial Loss Distribution Approach menggunakan matriks dimensi 8 (delapan) business lines (BL) dan 7 (tujuh) event type (ET). Dengan mencari distribusi yang paling fit untuk mengestimasi frekuensi dan severitas dari kerugian operasional, dari matriks [ ] hanya dapat mengisi 21 data pada matriks [ ] yang seluruhnya berjumlah 56. Pada Akhirnya hasil OpCaR dengan LDA jauh lebih efisien dan mendekati nilai real losses bank ABC dibandingkan dengan NSA.
ABSTRACT
One of the weaknesses from Basic Indicator Approach (BIA), Standardized Approach (SA), and Alternative Standardized Approach (ASA) is the use of gross income as proxy for operational risk exposure. Moreover, it does not take into account the fact that operational risk exposure increases non-linearly with the size of the bank. The Author tries to analyze the New Standardized Approach (NSA) that recently introduced by BCBS291 (October 2014) with two strategies consisting of, improvement of the SA calculations using business indicators (BI) as a proxy for operational risk capital calculation and improve calibration of coefficient in the measurement of operational risk capital based on range of gross income without seeing another β (beta) factor of 8 (eight) business lines. BI using three (3) macro components, there are interest component, services component, and financial component. NSA looks fair and more conservative in allocating Operational Capital at Risk (OpCaR) with higher number than BIA and SA. Moreover, we want to see the efficiency of OpCaR allocation in this study by analyzing AMA with Actuarial Loss Distribution Approach using dimensional matrix between 8 (eight) business lines (BL) and 7 (seven) event types (ET) as comparison. By searching for the most fit distribution to estimate the frequency and severity of operational losses of the matrix [ ], we get 21 instead of 56 data of matrix [ ]. At the end of analysis, OpCaR result from LDA is much more efficient and closer to the real value of the ABC bank losses compared to NSA., One of the weaknesses from Basic Indicator Approach (BIA), Standardized Approach (SA), and Alternative Standardized Approach (ASA) is the use of gross income as proxy for operational risk exposure. Moreover, it does not take into account the fact that operational risk exposure increases non-linearly with the size of the bank. The Author tries to analyze the New Standardized Approach (NSA) that recently introduced by BCBS291 (October 2014) with two strategies consisting of, improvement of the SA calculations using business indicators (BI) as a proxy for operational risk capital calculation and improve calibration of coefficient in the measurement of operational risk capital based on range of gross income without seeing another β (beta) factor of 8 (eight) business lines. BI using three (3) macro components, there are interest component, services component, and financial component. NSA looks fair and more conservative in allocating Operational Capital at Risk (OpCaR) with higher number than BIA and SA. Moreover, we want to see the efficiency of OpCaR allocation in this study by analyzing AMA with Actuarial Loss Distribution Approach using dimensional matrix between 8 (eight) business lines (BL) and 7 (seven) event types (ET) as comparison. By searching for the most fit distribution to estimate the frequency and severity of operational losses of the matrix [ ], we get 21 instead of 56 data of matrix [ ]. At the end of analysis, OpCaR result from LDA is much more efficient and closer to the real value of the ABC bank losses compared to NSA.] |
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