Origins of international factor structures

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Abstract

We show that exchange rate correlations tend to be explained by the global trade network while consumption correlations tend to be explained by productivity correlations. Sharing common trade linkages with other countries increases exchange rate correlations beyond bilateral linkages. We explain these findings using a model of the global trade network with market segmentation. Interdependent global production generates international comovements, while market segmentation disconnects the drivers of exchange rate correlations from the drivers of consumption correlations. Moreover, we show that the trade network generates common factors found in exchange rates. Our findings offer a trade-based account of the origins of international comovements and shed light on important frictions in international markets.

Section snippets

Reduced-form evidence on international comovements

Our goal is to understand the fundamental sources of comovements in asset prices and quantities across countries. These comovements can arise due to correlated shocks or due to the propagation of these shocks through international trade linkages. Countries with closer trade linkages or more correlated shocks could potentially have higher comovements in quantities and asset prices. To this end, we begin by documenting how cross-country correlations of asset prices and quantities are related to

A network model of international comovements

To explain the empirical findings in the previous section, we now present a general equilibrium model of international trade and asset prices. The model allows us to shine light on a number of the findings. First, we show how the structure of the global trade network interacts with primitive shocks to give rise to international comovements in asset prices and quantities. Second, we demonstrate why both bilateral and network closeness measures are important for understanding these comovements.

Model estimation

Having shown how our model generates the relation between trade network closeness and international comovements, we now turn to understanding further properties of our model. Our goal is to show that our model can reproduce a number of key moments in the data, taking the observed heterogeneity in the trade network and TFP correlations into account. In particular, we first discipline the model parameters using the observed network of trade linkages and other observable moments. Then, we estimate

Implications for international factor structures

The previous sections establish a link between the structure of the trade network and exchange rate correlations across countries. A salient fact about exchange rate correlations is that a large fraction of these correlations can be explained by a small number of common factors (Lustig, Roussanov, Verdelhan, 2011, Verdelhan, 2018). To investigate the origin of this factor structure, we next investigate if it is related to our network closeness measure. We find that the same factor structure

Conclusion

In this paper, we show that exchange rate comovements tend to be explained by the structure of the global trade network while consumption correlations tend to be explained by TFP correlations. Motivated by these findings, we present a multi-country model with segmented markets that replicates our new empirical findings. Furthermore, we show that the trade network structure generates common factors in exchange rates, as found in the data. These results provide new evidence on the key drivers of

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    Nikolai Roussanov was the editor for this article. For helpful comments and discussions we thank Patrick Bolton, Ric Colacito, Max Croce, Pasquale Della Corte, Xavier Gabaix, Tarek Hassan, Ralph Koijen, Nikolai Roussanov, Gill Segal, Andreas Stathopoulos, Rosen Valchev, Adrien Verdelhan, an anonymous referee, and seminar participants at Northwestern Kellogg, the Annual Conference in International Finance, the Columbia Workshop on New Methods in Empirical Finance, Hong Kong University of Science and Technology, Stevens Institute of Technology, UNC Kenan-Flagler, CIRANO-Walton Workshop on Networks in Economics and Finance, the Chicago Booth Asset Pricing Conference, Columbia University, the American Finance Association, and Network Science in Economics conference. We thank Steven Zheng and Cody Wan for excellent research assistance.

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