The bank approach to a credit obligor – a farm business – in the context of credit risk and capital adequacy

The objective of this paper is to enable a bank’s view towards a credit obligor. Banks are subject to a lot of financial risks. Credit risk is the most important one. Banks also have to manage the objective of maximum profit on one hand, the prudential rules on the other hand. Recently, the Bank for International Settlements submitted a new concept of prudential rules (The New Basel Capital Accord) that should be accepted by national regulators and applied from 2006/7. This concept brings relatively strict conditions which should improve bank management of credit risk but which are unpleasant for loaning of small and medium enterprises including agricultural ones that are mostly part of this category. Very important role will be still played by non-market supporting instruments, especially guarantees provided by sovereigns. They can improve the competitiveness of agricultural enterprises in the credit market.

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.

Statistical Models for Operational Risk

A statistical model is a possible representation (not necessarily complex) of a situation of the real world. Models are useful to give a good knowledge of the principal elements of the examined situation and so to make previsions or to control such a situation. In the banking sector, models, techniques and regulations have been developed for evaluating Market and Credit risks, for linking together risks, capital and profit opportunity. The regulations and vigilance standards on the capital have been developed from the Basel Committee founded at the end of 1974 by the G10. The standards for the capital’s measurement system were defined in 1988 with the “Capital Accord” (BIS, 1988); nowadays, it is supported from over 150 countries around the world. In January 2001 the Basel Committee published the document “The New Basel Capital Accord” (BIS, 2001), which is a consultative document to define the new regulation for the bank capital requirement. Such a document has been revisited many times (see BIS, 2005). With the new accord there is the necessity of appraising and managing, beyond the financial risks, also the category of the operational risks (OR) already responsible of losses and bankruptcies (Cruz (Ed.), 2004; Alexander (Ed.), 2003; Cruz, 2002).

Discriminant Analysis of Zero Recovery for China's NPL

Classification of whether recovery of non-performing loans (NPL) is zero or positive is not only important in management of non-performing loans, but also is essential for estimating recovery rate and implementing the new Basel Capital Accord. Based on the largest database of NPL's recovering information in China, this paper tries to establish discriminant models to predict the loan with zero recovery. We first use Step-wise discrimination method to select variables; then give an in-depth analysis on why the selected variables are important factors influencing whether a loan is zero or positive recovery rate. Using the selected variables, we establish two-type discriminant models to classify the NPLs. Empirical results show that both models achieve high prediction accuracy, and the characteristics of obligors are the most important factors in determining whether a NPL is positively recovered or zero recovered.