We study sovereign external debt crises over the past 200 years, with a focus on creditor losses, or "haircuts". Our sample covers 327 sovereign debt restructurings with external private creditors over 205 default spells since 1815. Creditor losses vary widely (from none to 100%), but the...

We use a network model of credit risk to measure market expectations of the potential spillovers from a sovereign default. Specifically, we develop an empirical model, based on the recent theoretical literature on contagion in financial networks, and estimate it with data on sovereign credit...

Using data from 56 nations over 45 years, we find that nations that are more likely to elect left wing governments face higher (and more volatile) sovereign spreads. To explain these facts, we build a sovereign default model in which two policymakers (left and right) alternate in power. The...

In the data sovereign default is always partial and varies in its duration. Debt levels during default episodes initially increase and do not experience reductions upon resolution. This paper presents a theory of sovereign default that replicates these properties, which are absent in standard...

Infrequent but turbulent overt sovereign defaults on domestic creditors are a "forgotten history" in Macroeconomics. We propose a heterogeneous-agents model in which the government chooses optimal debt and default on domestic and foreign creditors by balancing distributional incentives v. the...

International data suggests that fluctuations in the level and volatility of the world interest rate (as measured by the US treasury bill rate) are positively correlated with both the level and volatility of sovereign spreads in emerging economies. We incorporate an estimated time-varying...

Using a novel data set containing all bids by all bidders for Mexican government bonds from 2001 to 2017, we demonstrate that asymmetric information about default risk is a key determinant of primary market bond yields. Empirically, large bidders do not pay more for bonds than the average bidder...