This paper studies the impact of political risk on exchange rates. We focus on the Brexit Referendum as it provides a natural experiment where both exchange rate expectations and a time-varying political risk factor can be measured directly. We build a simple portfolio model which predicts that an increase in the Leave probability triggers a depreciation of the British Pound, both on account of exchange rate expectations and of political risk. We estimate the model for multilateral and bilateral British Pound exchange rates. The results confirm the model's main implications. When we extend the analysis to a portfolio model of multiple currencies, we find that the cross-currencies restrictions implied by the theory are not rejected by our system estimation. Moreover, the joint estimates of the multi-currency model in the presence of time-varying political risk premium are in many cases consistent with the Uncovered Interest Parity.

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents ("multilayer network"), which are summarized into a smaller number of network matrices ("network factors") through a novel tensor-based principal component approach. We provide consistency results for the estimation of the factors and the coefficients of the FNAR. Our approach combines two different dimension-reduction techniques and can be applied to ultra-high dimensional datasets. In an empirical application, we use the FNAR to investigate the cross-country interdependence of GDP growth rates based on a variety of international trade and financial linkages. The model provides a rich characterization of macroeconomic network effects and exhibits good forecast performance compared to popular dimension-reduction methods.

This paper provides new indices of global macroeconomic uncertainty and investigates the crosscountry transmission of uncertainty using a global vector autoregressive (GVAR) model. The indices measure the dispersion of forecasts that results from parameter uncertainty in the GVAR. Relying on the error correction representation of the model, we distinguish between measures of short-run and long-run uncertainty. Over the period 2000Q1-2016Q4, global short-run macroeconomic uncertainty strongly co-moves with financial market volatility, while long-run uncertainty is more highly correlated with economic policy uncertainty. We quantify global spillover effects by decomposing uncertainty into the contributions from individual countries. On average, over 40% of country-specific uncertainty is of foreign origin.

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