This paper examines the role of political affiliation in the extension of trade credit by Chinese firms. Using a data-set of over 70,000 firms over the period 2000-2007, we find that, because they benefit from easier access to short-term external funding, politically affiliated firms can extend...

This paper examines the role of political affiliation in the extension of trade credit by Chinese firms. Using a dataset of more than 70,000 firms over the period 2000-2007, we find that, because they benefit from easier access to short-term external funding, politically affiliated firms can...

We study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics,...

We study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics,...

We study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics,...

We study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics,...

We introduce endogenous fire sales into a simple network model. For any given initial distribution of shocks across the network, we develop a clearing algorithm to solve for the financial equilibrium. We then utilise the results to perform ex ante risk assessment and derive risk premia for every...