This paper investigates whether monetary policy shocks propagate through production networks in the Euro-area. The network is constructed using a comprehensive time-series of input-output linkages between European industries within and across 11 countries and the stock market reaction is exploited as a laboratory. High-frequency interest rate data is used to identify monetary policy shocks along two dimensions: one related to the current level of short-term rates (target) and a second related to expectations about the future path of monetary policy (path). First, using an event-study approach around ECB Monetary Policy Announcement dates and novel tools from spatial econometrics, this is the first paper to show that between 40% and 50% of the overall European stock market reaction is due to higher-order network effects. Second, the paper documents that differences in the position and centrality of industries in the Eurozone network, as well as in the global network are sources of heterogeneous spillover effects. Finally, the paper draws policy implications by showing that the network structure can be used to predict the aggregate magnitude of spillover effects.
This paper uses recent advances in the theoretical literature on financial networks and contagion to economically motivate the use of Spatial Autoregressions (SAR) to model and predict sovereign CDS spreads in the Eurozone. Following the work of Elliot, Jackson and Gollub (2015), the paper develops a simple structural network model of sovereign credit risk with financial cross-holdings and multiple equilibria. In this framework, spillovers from a severe financial shock occur via direct losses to assets held by creditors. Under simple regularity conditions of the spatial weights matrix, the paper shows that the theoretical network model naturally translates into a SAR, which models the interdependence between spreads by making each sovereign's CDS spread a function of the CDS spreads of its "network neighbors".
Using methods from graph theory and network analysis, I identify, visualize and analyze a network of residual returns of CRSP and COMPUSTAT firms. I compute a systemic measure of network centrality using principal components analysis. I find that firms that are more central in the network earn higher returns than firms that are located in the periphery. I rationalize this finding by arguing that central firms are characterized by higher market risk, because they are more exposed to idiosyncratic shocks that pass through the network. I investigate the implications for asset pricing models. I develop a factor-mimicking portfolio, weighted by centrality scores and show that such a factor helps explain the cross-section of firm returns. Furthermore, I demonstrate that the network centrality measure has predictive power in out-of-sample tests related to the recent financial crisis. Finally, I show that the returns of central companies are positively correlated with the market premium and future consumption growth.
This paper investigates whether exogenous tax shocks propagate through production networks. A static model with intermediate inputs predicts that the reaction of industries to tax shocks follows a spatial autoregression (SAR) and that the effect propagates upstream (from customers to suppliers). To test this empirically, we use quarterly sales data for US industries for the period 1972-2003. Our results suggest that production networks are an important mechanism through which tax shocks transmit to the real economy.
I investigate whether inventory leanness is reflected in the asset prices of manufacturing companies. I use deviations from expected industry-specific inventory levels to measure firms’ inventory strategy. Firms with low inventory stocks carry a 1.5 % risk premium, a result that cannot be explained by differences in operating costs, size, growth, profitability, investment and exposure to market risk. The paper argues that because inventories act as buffers against shocks, firms with lower levels of inventory are riskier. Consistent with this explanation, I find that following productivity shocks identified with the occurrence of natural disasters lean companies exhibit lower stock market returns and output than companies with high levels of inventory.