Measurement and Comparison of Operational Risk under the New Basel Capital Accord

Calculating Operational Risk Capital Charges for Indonesian Rural Banks

Journal of Public Administration and Governance ◽

10.5296/jpag.v1i1.784 ◽

2011 ◽

Vol 1 (1) ◽

pp. 286 ◽

Cited By ~ 2

Author(s):

Abdul Mongid ◽

Izah Mohd Tahir

Keyword(s):

Historical Data ◽

Operational Risk ◽

Banking Supervision ◽

Risk Capital ◽

Basel Committee ◽

Operational Risks ◽

Indicator Approach ◽

New Basel Capital Accord ◽

Capital Charge

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.

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Identifying the social, cultural and behavioural factors influencing an operational risk management framework implementation--Tanzania

PsycEXTRA Dataset ◽

10.1037/e508832013-001 ◽

2012 ◽

Author(s):

A. Van Niekerk ◽

M. May ◽

K. Moalusi ◽

D. Geldenhuys ◽

M. Levin

Keyword(s):

Risk Management ◽

Operational Risk ◽

Management Framework ◽

Operational Risk Management ◽

Risk Management Framework ◽

Factors Influencing ◽

The Social ◽

Behavioural Factors

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Functional and operational risk as a criterion for assessing the durability of an autonomous energy system

Safety and Reliability of Power Industry ◽

10.24223/1999-5555-2019-12-1-56-62 ◽

2019 ◽

Vol 12 (1) ◽

pp. 56-62 ◽

Cited By ~ 1

Author(s):

A. O. Nedosekin ◽

A. V. Smirnov ◽

D. P. Makarenko ◽

Z. I. Abdoulaeva

Keyword(s):

Service Life ◽

Structural Reliability ◽

Residual Life ◽

Fuzzy Random Variable ◽

Operational Risk ◽

Energy System ◽

Technical System ◽

Residual Service Life ◽

Technological Parameters ◽

Residual Service

The article presents new models and methods for estimating the residual service life of an autonomous energy system, using the functional operational risk criterion (FOR). The purpose of the article is to demonstrate a new method of durability evaluation using the fuzzy logic and soft computing framework. Durability in the article is understood as a complex property directly adjacent to the complex property of system resilience, as understood in the Western practice of assessing and ensuring the reliability of technical systems. Due to the lack of reliable homogeneous statistics on system equipment failures and recoveries, triangular fuzzy estimates of failure and recovery intensities are used as fuzzy functions of time based on incomplete data and expert estimates. The FOR in the model is the possibility for the system availability ratio to be below the standard level. An example of the evaluation of the FOR and the residual service life of a redundant cold supply system of a special facility is considered. The transition from the paradigm of structural reliability to the paradigm of functional reliability based on the continuous degradation of the technological parameters of an autonomous energy system is considered. In this case, the FOR can no longer be evaluated by the criterion of a sudden failure, nor is it possible to build a Markov’s chain on discrete states of the technical system. Assuming this, it is appropriate to predict the defi ning functional parameters of a technical system as fuzzy functions of a general form and to estimate the residual service life of the technical system as a fuzzy random variable. Then the FOR is estimated as the possibility for the residual life of the technical system to be below its warranty period, as determined by the supplier of the equipment.

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