Exchange rate predictability and dynamic Bayesian learning
研究投资者如何利用灵活贝叶斯推断从外汇市场预测信息中获利，基于多种向量自回归模型动态学习数据特征，在10国月度汇率预测中实现显著的经济收益。
Summary We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross‐section.