We introduce a new Economic Distress Index (EDI), which incorporates information from all economic sectors as a device for real-time monitoring of financial stability risks. Our approach is based on structural models of credit risk and incorporates market and balance sheet information from which we derive distance-to-defaults as uniform risk indicators across economic sectors, which form the basis of the EDI. Monetary financial institutions are the largest contributors to the EDI over the period from 1999 to 2023. In the post-Global Financial Crisis period, non-bank financial intermediaries emerge as the largest contributors to the EDI, consistent with broader developments that have contributed to the growth of non-bank financial intermediation. Using local projections, we show that the EDI also has significant predictive power for macroeconomic developments that originate primarily from high-stress regimes. Finally, we unpack that volatility is clearly the most important driver of the raw risk indicators, accounting on average for almost 80% of the explained variation.

We lay out a signal-extraction statistical model to estimate a factor summarizing conditions in U.S. credit markets. The factor provides a real-time gauge of “sentiment” in credit markets, above and beyond that attributable to contemporaneous economic conditions. Fluctuations in the credit market sentiment factor have strong asymmetric and nonlinear effects on economic activity, depending not only on the magnitude and sign of the credit market sentiment shock, but also on the current state of the economy. A positive credit market sentiment shock has a stronger and more persistent effect on real economic activity at the onset of a crisis than amidst an expansionary period. Instead, negative sentiment shocks have more persistent effects on activity during expansions than in recessions. Our credit market sentiment measure is a robust predictor of aggregate credit dynamics that outperforms financial indicators in the literature, and correlates positively with forecast errors of quarterly corporate earnings growth 12 months ahead. Finally, our results suggest that tightening monetary policy shocks temporarily deteriorate credit market sentiment, while easing monetary policy shocks do not have an effect on credit sentiment in the short run but end up deteriorating it in the longer run. Our findings about the link between monetary policy shocks and credit sentiment are consistent with monetary easing leading to the build-up of vulnerabilities in credit markets.

The Chicago Plan, proposed by leading economists during the Great Depression, envisaged the separation of banks into money banks with 100% reserve backing for deposits and credit banks financed through non-monetary liabilities. Fisher (1936) claimed four advantages: (1) Reduction of public debt through a debt-to-equity swap. (2) Reduction of private debts as money creation no longer requires debt creation. (3) Elimination of runs on the payment system. (4) Better control of credit-driven business cycles. Using a DSGE model of the US economy, we find strong support for all four claims. Furthermore, steady state output gains approach 17 percent and monetary policy is much more effective in response to every shock. Monetary policy improves welfare by combining a conventional Taylor rule with a countercyclical rule for the interest rate on treasury loans to credit banks.

We propose a functional false discovery rate method that uses multiple informative covariates to evaluate the conditional performance of predictive models while controlling for data snooping. Our method exhibits superior power and is robust to data dependence, estimation errors, and correlated covariates. Applying this method to a large set of currency technical trading rules, we construct a dynamic 30-currency portfolio yielding a Sharpe ratio of about one for roughly 50 years. We find that technical trading profitability decreases with trader computational power and capital account openness, suggesting that such profitability is related to how fast foreign exchange traders can detect specific market patterns.

This article presents a general conceptual and statistical framework for measuring the severity of financial crises on a continuous scale and in real time. It results in a composite index that operationalises the concept of systemic financial stress. The framework nests many existing financial stress and systemic risk indicators as special cases. The Composite Indicator of Systemic Stress (CISS) is introduced as an index design that provides crisis signals which are timely, robust and free of look–ahead bias. The CISS aggregates a representative set of market–specific stress indicators using their time–varying cross–correlations as systemic risk weights. Confirming its nature as a crisis severity measure, empirical analysis shows that the CISS has strong short-term predictive and nowcasting power for economic activity, and that these effects are stronger in bad states of the economy.