This study develops an early warning system for financial crises with a focus on small open economies. We contribute to the literature by developing macro-financial dynamic factor models that extract useful information from a rich but unbalanced mixed frequency data set that includes a range of global and domestic economic and financial indicators. The framework is applied to several Asian countries—Thailand, South Korea, Singapore, Malaysia, the Philippines and Indonesia. Logit regression models that use the extracted factors and other leading indicators have significant power in predicting systemic events. In-sample and out-of-sample test results indicate that the extracted factors help to improve the predictive power over a model that uses only sufficiently long history indicators. Importantly, models that include the dynamic factors yield consistently better out-of-sample crisis prediction results for key performance measures such as a usefulness index, the noise to signal ratio, and AUROC.

这项研究开发了针对金融危机的预警系统，重点是小型开放经济体。我们通过开发宏观金融动态因素模型为文献做出了贡献，该模型从丰富但不平衡的混合频率数据集中提取了有用的信息，该数据集包括一系列全球和国内经济和金融指标。该框架适用于多个亚洲国家-泰国，韩国，新加坡，马来西亚，菲律宾和印度尼西亚。使用提取的因子和其他主要指标的Logit回归模型在预测系统事件方面具有显着的功效。样本内和样本外的测试结果表明，相对于仅使用足够长的历史指标的模型，提取的因子有助于提高预测能力。重要的，