The forecasting of exchange rate returns has long been an issue in finance literature. The use of the best forecasting model is usually sensitive to the data frequency and the sample period used. Model evaluation is usually based on either minimizing error losses or maximizing profit strategies and other likelihood-based measures. Although, much work has been devoted to model evaluation based on maximizing profits strategies little to no work has been devoted to the issue of estimating a forecast model under the same principles. Here, we propose a Bayesian framework that estimates exchange rate models by considering measures such as directional accuracy and trading rules in a form of asymmetric loss functions. Estimation is implemented using Laplace-type estimators applied in cases where the likelihood function is not of a known form. We illustrate this method using simulated and real weekly exchange rate series. The results demonstrate that the use of profit maximizing strategies within estimation can significantly improve the forecasting ability of certain exchange rate models.

汇率收益的预测长期以来一直是金融文献中的一个问题。最佳预测模型的使用通常对使用的数据频率和样本周期很敏感。模型评估通常基于最小化错误损失或最大化利润策略和其他基于可能性的措施。虽然，基于最大化利润策略的模型评估已经投入了大量工作，但在相同原则下估计预测模型的问题几乎没有投入任何工作。在这里，我们提出了一个贝叶斯框架，该框架通过考虑方向准确性和不对称损失函数形式的交易规则等措施来估计汇率模型。在似然函数不是已知形式的情况下，使用拉普拉斯型估计器进行估计。我们使用模拟和真实的每周汇率序列来说明这种方法。结果表明，在估计中使用利润最大化策略可以显着提高某些汇率模型的预测能力。