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This book focused on estimation of extreme risk in financial time series data. The conditional Value at Risk and Conditional Expected Shortfall have been applied to estimate extreme risk in exchange rate returns. The Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model is applied to estimate current volatility in daily returns and Extreme Value Theory (EVT) approach is applied to estimate quantiles of innovations. Therefore, the estimated Volatility and quantiles are combined to obtain conditional Value at risk and conditional expected shortfall estimates. The results are applied to real data to estimate extreme risk in Rwanda Exchange rate process.