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Working Paper
Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs
Credit gaps are good predictors for financial crises, and banking regulators recommend using them to inform countercyclical capital buffers for banks. Researchers typically create credit gap measures using trend-cycle decomposition methods, which require many modelling choices, such as the method used, and the smoothness of the underlying trend. Other choices hinge on the tradeoffs implicit in how gaps are used as early warning indicators (EWIs) for predicting crises, such as the preference over false positives and false negatives. We evaluate how the performance of credit-gap-based EWIs for ...
Working Paper
Risk Management for Sovereign Debt Financing with Sustainability Conditions
We develop a model of debt sustainability analysis with optimal financing decisions in the presence of macroeconomic, financial and fiscal uncertainty. We define a coherent measure of refinancing risk, and trade off the risks of debt stock and flow dynamics, subject to debt sustainability constraints and endogenous risk and term premia. We optimize both static and dynamic financing strategies, compare them with several simple rules and consol financing to demonstrate economically significant effects of optimal financing, and show that the stock-flow tradeoff can be critical for ...
Working Paper
Recreating Banking Networks under Decreasing Fixed Costs
Theory emphasizes the central role of the structure of networks in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: Banks? assets and liabilities are typically known, but not who is lending how much and to whom. We first show how to simulate realistic networks that are based on balance-sheet information by minimizing costs where there is a fixed cost to forming a link. Second, we also show how to do this for a model with fixed costs that are decreasing in the number of links. To approach the optimization problem, we ...