TY - BOOK ID - 84543283 TI - Stochastic Volatilities and Correlations, Extreme Values and Modeling the Macroeconomic Environment, Under Which Brazilian Banks Operate AU - Souto, Marcos. AU - Barnhill, Theodore. AU - International Monetary Fund. PY - 2007 SN - 1462341683 1452706719 1283516241 9786613828699 1451913060 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Banks and Banking KW - Investments: Energy KW - Investments: Metals KW - Foreign Exchange KW - Money and Monetary Policy KW - Energy: General KW - Metals and Metal Products KW - Cement KW - Glass KW - Ceramics KW - Financing Policy KW - Financial Risk and Risk Management KW - Capital and Ownership Structure KW - Value of Firms KW - Goodwill KW - Banks KW - Depository Institutions KW - Micro Finance Institutions KW - Mortgages KW - Monetary Policy, Central Banking, and the Supply of Money and Credit: General KW - Investment & securities KW - Currency KW - Foreign exchange KW - Financial services law & regulation KW - Banking KW - Monetary economics KW - Oil KW - Gold KW - Credit risk KW - Commodities KW - Financial regulation and supervision KW - Credit KW - Money KW - Petroleum industry and trade KW - Financial risk management KW - Banks and banking KW - United States KW - Stochastic models. KW - Econometric models. UR - https://www.unicat.be/uniCat?func=search&query=sysid:84543283 AB - Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably well the historical evolution of volatilities by changing a decay factor appropriately. Finally, we implement a simple stochastic volatility model and simulate the credit transition matrix for two large Brazilian banks and show that this methodology has the potential to improve simulated transition probabilities as compared to the constant volatility case. In particular, it can shift CTM probabilities towards lower credit risk categories. ER -