In this thesis we develop three essays on risk management in some emerging countries. On the first one, using models of the GARCH family, we verified that the increase in relative weights assigned to the earlier observations due to the increase of the forecast horizon results in better estimates of volatility. Through the use of seven forecasting models of volatility and return series of financial markets assets (shares of Petrobras and Vale, Bovespa index, exchange rate Real/Dollar, 1-year and 3 years interest rates of Brazilian Government bonds issued in Reais) the estimates obtained in the sample (in-sample) were compared with observations outside the sample (out-of-sample). Based on this comparison, it was found that the best estimates of expected volatility were obtained predominantly by two models that allow its parameters to vary depending on the forecast horizon: the modified EGARCH model (exponential generalized autoregressive conditional heteroskedastic) and the ARLS model proposed by Ederington and Guan (2005). We conclude that the use of traditional forecasting models of volatility, which keeps unchanged relative weights assigned to both old and new observations, regardless of the forecast horizon, was inappropriate. On the second essay we compared the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock índices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizons; that the error distribution used may influence the decision about the best model; and that only for FIGARCH models the occurrence of underestimation of the true VaR is less frequent with increasing time horizon. Regarding the third essay, we realized that risk management is a subject that has long been part of the day-to-day activities of financial and nonfinancial institutions, yet the use of risk metrics is not common among public agencies. Considering this gap, and the importance of the issue for the proper management of public resources, the purpose of this third essay is to estimate, in a single value, the liquidity risk of a public agency, in this case, the Brazilian Navy, and to identify the sources of risk. To do this, the exposure-based Cash-Flow-at- Risk (CFaR) model has been developed, which, in addition to summarizing the liquidity risk estimation in a single value, helps in managing risk by providing additional information about the exposure of the organization s cash flow to various risk factors. Using quarterly data for the period between the first quarter of 1999 and the fourth quarter of 2013, the macroeconomics and market risk factors that impact the Navy s cash flow were identified. Moreover, the CFaR was calculated at a 95 percent confidence level for a period of one quarter ahead.