The COVID-19 pandemic and associated policy responses triggered a historically large wave of capital reallocation between markets, asset classes, and industries. Using high-frequency country-level data, we examine if and how the number of infections, the stringency of the lockdown, and the fiscal and monetary policy response determined the dynamics of portfolio flows, market-implied sovereign risk, and stock prices. We find that these factors played an important role, particularly for emerging markets. Our results indicate that domestic infections had an initial negative impact on flows. Cumulatively, however, the effect was positive and reflected increased demand for financing by affected economies. We also find that both lockdown and fiscal measures supported portfolio flows, driven by an increased supply of funds. Bonds, not equities, were the primary driver of portfolio flows, highlighting a pattern of reallocation to safety. Finally, we show that monetary policy loosening in developed markets led to a cumulative decline in flows, as investors searched for higher yield.
joint with Nina Biljanovska and Francesco Grigoli, Review of International Economics, early view, 2021
High levels of economic policy uncertainty (EPU) in various parts of the world revamped the debate about its impact on economic activity. Employing heterogeneous panel structural vector autoregressions, we test for EPU spillovers on other countries’ economic activity. EPU reduces growth in real output, private consumption, and private investment, with spillovers from abroad accounting for about two‐thirds of the effect. Using local projections, we show that EPU surges in the United States, Europe, and China reduce economic activity in the rest of the world, with the effects being mostly felt in Europe and the Western Hemisphere.
IHEID Working Paper HEIDWP19-2019
Are empirical measures of uncertainty informative about risks to future economic activity? I use quantile regression analysis and density forecasts on United States data to show that the relationship between macroeconomic uncertainty and future GDP growth is nonlinear and asymmetric. The left tail of the distribution of future GDP growth is highly responsive to fluctuations in macroeconomic uncertainty, whereas the right tail is relatively stable. As such, macroeconomic uncertainty predicts downside risks to growth but is less informative about upside risks. When combined with an index of financial conditions—a previously proposed predictor of downside risks to growth—macroeconomic uncertainty carries a larger weight in the optimal predictive density. Finally, I provide evidence that alternative empirical measures of uncertainty, such as economic policy uncertainty and geopolitical risk, do not predict risks to the economic outlook. These results hold for a larger sample of countries and underline the importance of differentiating between measures of uncertainty when predicting risks to growth.
Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP
joint with Seton Leonard, IHEID Working Paper HEIDWP13-2017
This paper presents a novel dynamic factor model for non-stationary data. We begin by constructing a simple dynamic stochastic general equilibrium growth model and show that we can represent and estimate the model using a simple linear-Gaussian (Kalman) filter. Crucially, consistent estimation does not require differencing the data despite it being cointegrated of order 1. We then apply our approach to a mixed frequency model which we use to estimate monthly U.S. GDP from May 1969-2016 using 171 series with an emphasis on housing related data. We suggest our estimates may, at a quarterly rate, in fact be more accurate than measurement error prone observations. Finally, we use our model to construct pseudo real-time GDP nowcasts over the 2007-2009 financial crisis. This last exercise shows that a GDP index, as opposed to real time estimates of GDP itself, may be more helpful in highlighting changes in the state of the macroeconomy.