Explaining Bank Failures in Brazil: Micro, Macro and Contagion Effects (1994-1998)

We apply duration (survival) models with exponential hazard and exponential piecewise-constant hazard functions to study the determinants of bank failure over the period 1994 to 1998 in Brazil. The models deal empirically with left censoring in the data. We control for macroeconomic conditions and contagion effects, besides bank-specific factors. Our results indicate that foreign banks have distinct empirical survival functions relatively to other banks. For Brazil, macroeconomic and bank-level covariates explain the likelihood and timing of bank failure. Our indicator of system-wide financial fragility (IFF) suggests that the banking industry faced increased fragility after November 1995. We find evidence that the Program of Incentives to the Restructuring and Strengthening of the National Financial System (Proer) was able to distinguish solvent from insolvent banks.

Macroeconomic Policy, Growth and Income Distribution in the Brazilian Economy in the 2000s

The Brazilian economy grew by 4.2 percent annually from 2004-2010, more than double its annual growth from 1999-2003 or indeed its growth rate over the prior quarter century. This growth was accompanied by a significant reduction in poverty and extreme poverty, especially after 2005, as well as reduced inequality. This paper looks at the combination of external changes and changes in macroeconomic policy that contributed to these results

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Monetary policy report to the Congress, February 17, 2000 (semi-annual Humphrey-Hawkins report)

The U.S. economy posted another exceptional performance in 1999. The ongoing expansion appears to have maintained strength into early 2000 as it set a record for longevity, and-aside from the direct effects of higher crude oil prices-inflation has remained subdued, in marked contrast to the typical experience during previous expansions. The past year brought additional evidence that productivity growth has improved substantially since the mid-1990s, boosting living standards while helping to hold down increases in costs and prices despite very tight labor markets. To maintain the low inflation environment that has been so important to the sustained health of the current expansion, the Federal Open Market Committee has implemented four quarter-point increases in the intended federal funds rate since mid-1999; the most recent of these came at the beginning of February 2000. In total, the federal funds rate has been raised 1 percentage point, although, at 5 _ percent, it stands only ¬ point above its level just before the autumn 1998 financial market turmoil. At its most recent meeting, the FOMC indicated that risks appear to remain on the side of heightened inflation pressures, so it will need to remain especially attentive to developments in this regard.Economic conditions - United States ; Monetary policy - United States

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

TOWARDS A NEW SET OF LEADING INDICATORS OF CURRENCY CRISIS FOR DEVELOPING COUNTRIES: AN APPLICATION TO ARGENTINA

Currency and financial crises are determinants of growth and development, mainly in developing countries subject to shocks, contagion and volatility. A relevant issue when trying to do the implementation of development policies is to anticipate or forecast the occurrence of currency crises that could turn good ideas into failure. This type of crises have strong negative economic, social and political consequences. This paper takes a look in the leading indicators literature and shows that this approach failed in predicting the Argentinean collapse of 2001-2002. We also show that particular features of the Argentinean economy needed of different indicators to forecast the collapse of the currency board system. The paper also developes some new indicators to include in an Early Warning System that can take on account specific features of Argentina´s economy. This indicators can be integrated into a wider set in order to be a useful tool for policymakers and authorities in Argentina and in other developing countries in the planification and implementation of development policies and programs.currency crisis - exchange rate - leading indicators - Argentina

Identifying Volatility Risk Premium from Fixed Income Asian Options

We provide approximation formulas for at-the-money asian option prices to extract volatility risk premium from a joint dataset of bonds and option prices. The dynamic model generates stochastic volatility and a time-varying volatility risk premium, which explicitly depends on the average cross section of bond yields and on the time series behavior of option prices. When estimated using a joint dataset of Brazilian local bonds and asian options, the model generates bond risk premium strongly correlated (89%) with a widely accepted emerging markets benchmark index, and a negative volatility risk premium implying that investors might be using options as insurance in this market. Volatility premium explains a significant portion (32.5%) of bond premium, confirming that options are indeed important to identify risk premium in dynamic term structure models.

Decision Support Systems for Risk Assessment in Credit Operations Against Collateral

With the global economic crisis, which reached its peak in the second half of 2008, and before a market shaken by economic instability, financial institutions have taken steps to protect the banks’ default risks, which had an impact directly in the form of analysis in credit institutions to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most banks use a graded scale of customer risk, which determines the provision that banks must do according to the default risk levels in each credit transaction. The credit analysis involves the ability to make a credit decision inside a scenario of uncertainty and constant changes and incomplete transformations. This ability depends on the capacity to logically analyze situations, often complex and reach a clear conclusion, practical and practicable to implement. Credit Scoring models are used to predict the probability of a customer proposing to credit to become in default at any given time, based on his personal and financial information that may influence the ability of the client to pay the debt. This estimated probability, called the score, is an estimate of the risk of default of a customer in a given period. This increased concern has been in no small part caused by the weaknesses of existing risk management techniques that have been revealed by the recent financial crisis and the growing demand for consumer credit.The constant change affects several banking sections because it prevents the ability to investigate the data that is produced and stored in computers that are too often dependent on manual techniques. Among the many alternatives used in the world to balance this risk, the provision of guarantees stands out of guarantees in the formalization of credit agreements. In theory, the collateral does not ensure the credit return, as it is not computed as payment of the obligation within the project. There is also the fact that it will only be successful if triggered, which involves the legal area of the banking institution. The truth is, collateral is a mitigating element of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations on time. For the creditor, it generates liquidity security from the receiving operation. The measurement of credit recoverability is a system that evaluates the efficiency of the collateral invested return mechanism. In an attempt to identify the sufficiency of collateral in credit operations, this thesis presents an assessment of smart classifiers that uses contextual information to assess whether collaterals provide for the recovery of credit granted in the decision-making process before the credit transaction become insolvent. The results observed when compared with other approaches in the literature and the comparative analysis of the most relevant artificial intelligence solutions, considering the classifiers that use guarantees as a parameter to calculate the risk contribute to the advance of the state of the art advance, increasing the commitment to the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável de implementar. Os modelos de Credit Score são usados para prever a probabilidade de um cliente propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de um cliente em um determinado período. A mudança constante afeta várias seções bancárias, pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores que frequentemente dependem de técnicas manuais. Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não “garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito, como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que avalia a eficiência do mecanismo de retorno do capital investido em garantias. Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de tomada de decisão antes que a operação de crédito entre em default. Os resultados observados quando comparados com outras abordagens existentes na literatura e a análise comparativa das soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte, aumentando o comprometimento com as instituições financeiras

Análise do risco de inadimplência na utilização de cartões de crédito

ABSTRACT. This paper analyzes the risk of default in the use of credit cards generating probabilities of delay in payment with different variables such as age, gender, credit limit and annual income. The behavior of debtors who use credit cards is studied identifying changes in states of delay of risk levels. A multi-state model of Markov was used to perform the analysis. The study was applied to credit card usage records of individuals in 121 commercial and financial institutions. This research identifies the patterns of use by credit card customers and provides valuable inputs to help financial institutions understand the phenomenon of default risk.RESUMO. Este trabalho analisa o risco de inadimplência na utilização de cartões de crédito gerando probabilidades de atraso no pagamento com diferentes variáveis tais como idade, sexo, limite de crédito e rendimento anual. O comportamento dos devedores que utilizam cartões de crédito é estudado identificando alterações nos estados de atraso dos níveis de risco. Foi utilizado um modelo multiestado de Markov para realizar a análise. O estudo foi aplicado aos registos de utilização de cartões de crédito de indivíduos em 121 instituições comerciais e financeiras. Este estudo identifica os padrões de utilização pelos clientes de cartões de crédito e fornece dados valiosos para ajudar as instituições financeiras a compreender o fenómeno do risco de inadimplência