Forecasting exchange rates using asymmetric losses: A Bayesian approach
提出一种贝叶斯框架，在估计汇率模型时考虑方向准确性和交易规则等非对称损失函数，使用拉普拉斯型估计器处理未知似然函数，模拟和实际周汇率数据表明该方法能显著提升预测能力。
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.