Quantile forecasts are central to risk management decisions because of the widespread\ud use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to\ud forecast volatility, and the method of computing quantiles from the volatility forecasts. In\ud this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns\ud of five currencies. The forecasting models that have been used in recent analyses of the\ud predictability of daily realized volatility permit a comparison of the predictive power of\ud different measures of intraday variation and intraday returns in forecasting exchange rate\ud variability. The methods of computing quantile forecasts include making distributional\ud assumptions for future daily returns as well as using the empirical distribution of predicted\ud standardized returns with both rolling and recursive samples. Our main findings are that the\ud Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts\ud for currencies which experience shifts in volatility, such as the Canadian dollar, and that\ud the use of the empirical distribution to calculate quantiles can improve forecasts when there\ud are shifts
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