Identifying Global and Local Shocks in International Financial Markets: A New Approach using General Dynamic Factor Models

This article proposes a new approach for identifying global and local shocks in international financial markets using general dynamic factor models. The authors argue that traditional methods for identifying these shocks, such as principal component analysis and vector autoregression models, have limitations in capturing the complex interdependencies among different financial variables and the dynamics of the shocks.

The proposed method uses a general dynamic factor model that allows for both global and local factors, and incorporates a time-varying covariance matrix to capture the changing nature of the shocks over time. The authors apply this approach to a dataset of 30 international stock markets, and find that it outperforms traditional methods in identifying both the global and local shocks.

The results show that global shocks have a significant impact on international stock markets, and that the transmission of these shocks varies across different regions and countries. The authors also find evidence of contagion effects during periods of crisis, where local shocks in one country can spread to other countries.

Overall, this article provides a new method for identifying and analyzing shocks in international financial markets, which can be useful for policymakers and investors in understanding the dynamics of these markets and developing strategies for managing risks.