A atividade bancária envolve em suas operações diversas formas de riscos. Dentre esses riscos está o risco de crédito, ou risco de inadimplência, presente em transações em que a instituição se torna credora. Sua mensuração exige que se tenha conhecimento da probabilidade de inadimplência associada a cada classificação. Neste trabalho são apresentadas as principais metodologias de quantificação do risco de crédito como Credit Metrics, KMV, Credit Portfolio View e CreditRisk+. Esta última metodologia, juntamente com o conceito de RAROC (Risk Adjusted Return on Capital), é aplicada a um portfólio de financiamentos rurais e agroindustriais à pessoa jurídica, evidenciando o capital econômico alocado (CEA) e o spread necessário para cobrir as perdas esperadas e inesperadas. Esse portfólio totaliza R$ 1,42 bilhões referentes ao mês de março de 2003. São construídos dois cenários com diferentes índices de inadimplência associados a cada classificação. O primeiro aproxima os percentuais de provisionamento definidos pelo Banco Central do Brasil (BACEN) para índices de inadimplência e o segundo utiliza os percentuais obtidos por uma matriz de migração de clientes vinculados às atividades rurais e agroindustriais para o período de 2000 a 2002. Observa-se como resultado que ocorre uma maior alocação de capital econômico para setores rurais e agroindustriais que possuem risco concentrado como o setor de fumo, com total de financiamentos em R$202,9 milhões e CEA de R$78,9 milhões e R$114,0 milhões para o cenário 1 e cenário 2, respectivamente. As modalidades de financiamentos rurais e agroindustriais de custeio e desconto de Nota Promissória Rural (NPR) são responsáveis por cerca de 75% do total do portfólio. No entanto, estas modalidades apresentam a necessidade de um spread menor para cobrir as perdas esperadas e inesperadas com crédito, sinalizando uma composição de clientes com melhor classificação. Observa-se também que os menores spreads ocorrem nos setores de industrialização, principalmente na indústria de cigarros, laticínios, soja e derivados, e resinas de fibras e fios sintéticos. Já os setores como fumo, moagem de trigo e abate de aves, tiveram maiores spreads. / The banking activity involves several forms of risk in its operation. Among these risks, there is one called the credit risk, or the default risk. Its measurement requires that the financial institution owns knowledge about the default probability associated with each rating class. In this research, four models of credit risk are discussed: Credit Metrics, KMV, Credit Portfolio View, and CreditRisk+. The last model, the CreditRisk+, associated with the concept of Risk Adjusted Return on Capital (RAROC) is applied to a financial portfolio to the farm and agribusiness sectors. Under this analysis, the indicators of allocated economic capital and spreads are discussed with respect to the expected and unexpected losses. The data used in this analysis are unique and represent the total amount of loans as of March 2003, R$ 1.42 billions, made by a specific commercial bank to the commercial farms and agribusiness companies. Two scenarios are evaluated considering different level of default risks associated with each rating class. The first scenario uses the provisional indexes defined by the Brazilian Central Bank. The second scenario uses a computable migration matrix over the period 2000 through 2002. The results show that the higher amount of allocated economic capital occurs in the tobacco sector in which the total amount of loans is R$ 202.9 millions. The total amount of allocated economic capital is R$ 78.9 million and R$ 114 million under scenarios 1 and 2 respectively. The data used in this study show that seventy-five percent of the totals of loans has as a purpose for operating expenses and discount of agribusiness promissory notes. These loans show the lowest spreads to cover expected and unexpected losses with the credit operation. The lowest spread is observed at the following processing sectors: tobacco industries, milk and soybean processors, and fiber resins and synthetic fibers. On the other hand, the sectors that show the highest spreads are: tobacco farms, wheat processors, and poultry slaughter houses.

A atividade bancária envolve em suas operações diversas formas de riscos. Dentre esses riscos está o risco de crédito, ou risco de inadimplência, presente em transações em que a instituição se torna credora. Sua mensuração exige que se tenha conhecimento da probabilidade de inadimplência associada a cada classificação. Neste trabalho são apresentadas as principais metodologias de quantificação do risco de crédito como Credit Metrics, KMV, Credit Portfolio View e CreditRisk+. Esta última metodologia, juntamente com o conceito de RAROC (Risk Adjusted Return on Capital), é aplicada a um portfólio de financiamentos rurais e agroindustriais à pessoa jurídica, evidenciando o capital econômico alocado (CEA) e o spread necessário para cobrir as perdas esperadas e inesperadas. Esse portfólio totaliza R$ 1,42 bilhões referentes ao mês de março de 2003. São construídos dois cenários com diferentes índices de inadimplência associados a cada classificação. O primeiro aproxima os percentuais de provisionamento definidos pelo Banco Central do Brasil (BACEN) para índices de inadimplência e o segundo utiliza os percentuais obtidos por uma matriz de migração de clientes vinculados às atividades rurais e agroindustriais para o período de 2000 a 2002. Observa-se como resultado que ocorre uma maior alocação de capital econômico para setores rurais e agroindustriais que possuem risco concentrado como o setor de fumo, com total de financiamentos em R$202,9 milhões e CEA de R$78,9 milhões e R$114,0 milhões para o cenário 1 e cenário 2, respectivamente. As modalidades de financiamentos rurais e agroindustriais de custeio e desconto de Nota Promissória Rural (NPR) são responsáveis por cerca de 75% do total do portfólio. No entanto, estas modalidades apresentam a necessidade de um spread menor para cobrir as perdas esperadas e inesperadas com crédito, sinalizando uma composição de clientes com melhor classificação. Observa-se também que os menores spreads ocorrem nos setores de industrialização, principalmente na indústria de cigarros, laticínios, soja e derivados, e resinas de fibras e fios sintéticos. Já os setores como fumo, moagem de trigo e abate de aves, tiveram maiores spreads. / The banking activity involves several forms of risk in its operation. Among these risks, there is one called the credit risk, or the default risk. Its measurement requires that the financial institution owns knowledge about the default probability associated with each rating class. In this research, four models of credit risk are discussed: Credit Metrics, KMV, Credit Portfolio View, and CreditRisk+. The last model, the CreditRisk+, associated with the concept of Risk Adjusted Return on Capital (RAROC) is applied to a financial portfolio to the farm and agribusiness sectors. Under this analysis, the indicators of allocated economic capital and spreads are discussed with respect to the expected and unexpected losses. The data used in this analysis are unique and represent the total amount of loans as of March 2003, R$ 1.42 billions, made by a specific commercial bank to the commercial farms and agribusiness companies. Two scenarios are evaluated considering different level of default risks associated with each rating class. The first scenario uses the provisional indexes defined by the Brazilian Central Bank. The second scenario uses a computable migration matrix over the period 2000 through 2002. The results show that the higher amount of allocated economic capital occurs in the tobacco sector in which the total amount of loans is R$ 202.9 millions. The total amount of allocated economic capital is R$ 78.9 million and R$ 114 million under scenarios 1 and 2 respectively. The data used in this study show that seventy-five percent of the totals of loans has as a purpose for operating expenses and discount of agribusiness promissory notes. These loans show the lowest spreads to cover expected and unexpected losses with the credit operation. The lowest spread is observed at the following processing sectors: tobacco industries, milk and soybean processors, and fiber resins and synthetic fibers. On the other hand, the sectors that show the highest spreads are: tobacco farms, wheat processors, and poultry slaughter houses.

Thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Agriculture (Agricultural Economics) at Stellenbosch University / Thesis (MScAgric (Agricultural Economics))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The main objective of the research is to understand the concept of cooperative conversions and compare the performance of converted cooperatives to those that never converted using financial accounting analysis and organisational dynamism. Even though the differences were relatively small, companies had the strongest relative financial performance than cooperatives. Companies had the strongest performances in asset and revenue growth. Average revenue growth for companies from 2004 to 2007 was 29% as compared to 15% by cooperatives and asset growth was 25% for companies compared to 12.5% by cooperatives. Results further indicate that for the past two years, cooperatives seem to be reporting decreasing performance in most of the financial ratios analysed. Thus, based on results from the financial analysis, operating as a company or converting from a cooperative to a company could result in slight increases in financial performance. Rapid change presents various challenges and opportunities for businesses in today‘s dynamic environment. As a result, business dynamism is becoming an increasingly important aspect and factor in determining success. Based on a dynamism score card, the study shows that companies are by far much more dynamic than cooperatives, with a score of 83.75 compared to 62.33 out of 100 respectively. However, cooperatives compare relatively well to companies in as far as organisational strategy, management, organisational structure and culture. Their limitations come from their property rights framework which is by far less dynamic than that of companies owing to the limitations and constraints of the Cooperatives Act (Act 14 of 2005). The main shortcomings of cooperative property rights were that of not allowing external investors into the cooperative and the one member one vote principle for primary cooperatives or the 15% cap for secondary cooperatives. / AFRIKAANSE OPSOMMING: Die vernaamste doelwit van hierdie navorsing was om die konsep van koöperatiewe omsettings te verstaan en die prestasie van omsette koöperasies te vergelyk met dié wat nog nooit deur middel van finansiële rekeningkundige analise en organisatoriese dinamisme omgesit is nie. Hoewel die verskille relatief klein was, het maatskappye die sterkste relatiewe finansiële prestasie gehad in vergelyking met koöperasies. Maatskappye het ook die sterkste prestasie in bate- en inkomstegroei getoon. Gemiddelde inkomstegroei vir maatskappye vanaf 2004 tot 2007 was 29%, in vergelyking met 15% vir koöperasies, terwyl bategroei vir maatskappye 25% was in vergelyking met 12.5% vir koöperasies. Die resultate toon verder dat koöperasies oor die afgelope twee jaar verminderde prestasie blyk te rapporteer in die meerderheid van die finansiële verhoudings wat geanaliseer is. Dus, op grond van die resultate van die finansiële analise, sal funksionering as ‘n maatskappy of omsetting van ‘n koöperasie na ‘n maatskappy kan lei tot ‘n effense verhoging in finansiële prestasie. Snelle verandering bied verskeie uitdagings en geleenthede vir maatskappye in die huidige dinamiese omgewing. Gevolglik is sakedinamisme besig om ‘n toenemend belangrike aspek en faktor in die bepaling van sukses te word. Op die basis van ‘n dinamisme-telkaart het hierdie studie getoon dat maatskappye baie meer dinamies is as koöperasies, met ‗n telling van 83.75 in vergelyking met 62.33 uit 100 onderskeidelik. Koöperasies vergelyk egter relatief goed met maatskappye in soverre dit organisatoriese strategie, bestuur, organisatoriese struktuur en kultuur behels. Hulle beperkings kom van hulle eiendomsregraamwerk, wat baie minder dinamies is as dié van maatskappye op grond van die beperkings van die Wet op Koöperasies (Wet 14 van 2005). Die vernaamste tekorte van koöperatiewe eiendomsregte is dat hulle nie eksterne beleggers in die koöperasie toelaat nie en die beginsel van een lid, een stem vir primêre koöperasies of die 15% perk op sekondêre koöperasies.