We study the impact of cyclical systemic risks on banks’ profitability in the euro

area within a panel quantile local projection setting, with the ultimate goal to inform

the calibration of the Countercyclical Capital buffer (CCyB). Compared to previous

studies, we augment our model to control for unobserved bank-specific characteristics

and year-fixed effects and find a lower degree of heterogeneity in the estimated effects

across the conditional distribution of bank returns on assets. We propose a simple

yet intuitive framework to calibrate the CCyB through the cycle, including the so

called "positive neutral" rate. The model suggests a target positive neutral rate for

the euro area ranging from 1.1% to 1.8%. Furthermore, the calibrated CCyB rates

are consistent with the evolution of domestic cyclical systemic risks in the countries

considered. The results further show that the adoption of a positive neutral CCyB

approach allows for an earlier and more gradual build-up of the buffer, but does not

lead to higher CCyB requirements at the peak of the cycle. Importantly, a positive

neutral CCyB strategy would have implied that most euro area countries would have

had a positive CCyB in place at the onset of the COVID-19 pandemic.

Recent technological innovations and demographic trends have fueled an unprecedented surge in private, non-bank credit, driven in part by distributed ledger technology (DLT) and tokenization. In contrast to traditional bank lending which expands the money supply through fractional reserve banking this new mode of nancing reallocates existing funds without creating additional money. Using a stylized Quantity Theory of Money framework, we demonstrate how this shift triggers a critical decoupling between money supply and real output, generating de ationary pressures that could ultimately sti e economic growth. Our ndings raise important questions about the e cacy of conventional monetary policy in an era increasingly dominated by DLT-driven private credit. We conclude by proposing policy options designed to recalibrate money creation mechanisms, ensuring monetary stability and sustained growth.

We analyse the effect of platform fragmentation due to the introduction of fast payment systems (FPS) -- such as FedNow, Pix and UPI -- on the cost of processing payments. Funds sent using FPS offer the recipient greater convenience because they are available for use in real time. However, this could come at the cost of making legacy payment platforms more expensive, owing to the reduction in netting opportunities arising from payment migration to the FPS. We highlight that whether the benefits of providing a choice of payment platforms outweigh the negative externalities imposed by each platform on the other depend on the cost and benefit of immediate access to funds, as well as the settlement protocols of the payment platforms.

Using a proprietary gilt market dataset, this paper identifies how market microstructure frictions affect system-wide market liquidity. Starting from the structure of the repo market, individual dealer constraints and intermediation frictions generate inefficiencies with welfare implications through three independent, albeit related, mechanisms. More broadly, we provide a new perspective on how financial imperfections in the money market, measured by market power and relationship trading, affect the real economy through shocks to individual dealer capacity.

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.