Abstract: Geopolitical risk is a major concern for the euro area, yet widely used measures largely reflect a US perspective. We introduce a geopolitical risk indicator tailored to the euro area using local European news sources. Shocks to this index have significant recessionary and inflationary consequences in the euro area, effects that would be missed when relying on the corresponding US-based measure. We estimate that the Russo Ukrainian War imposed substantial output losses and inflationary pressures on the euro area in 2022. Combining structural scenario analysis with end-of-sample now casting, we show that euro area prospects are highly sensitive to future developments in geopolitical risk. We complement these analyses with two news-based measures of sanctions intensity and shortages for the euro area.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6764858

Abstract: The US dollar (USD) is involved in 88% of global foreign exchange transactions, partly due to its role as a vehicle currency. Using high-frequency data from primary interdealer platforms, we develop a novel methodology to identify USD cross-trades. We show both theoretically and empirically that such trades can generate price fluctuations in USD exchange rates. Employing an instrumental variables approach, we find that increased cross-trading activity amplifies aggregate USD volatility. These results highlight a fundamental trade-off: while dollar dominance enhances market liquidity, it also increases the currency’s exposure to shocks originating in other currency pairs.

https://www.snb.ch/en/publications/research/working-papers/2026/working_paper_2026_05

Abstract: I propose identifying structural vector autoregressions using ‘shock-percentile’ restrictions. These restrictions require the realisation of a structural shock in a selected episode to lie in the tail of the shock’s historical distribution, representing the belief that a relatively large shock has occurred. I argue that shock-percentile restrictions are an attractive alternative to imposing numeric bounds on shock magnitudes, which are difficult to credibly elicit. Simulations demonstrate the potential for shock-percentile restrictions to provide identifying information. In two empirical applications, I exploit shock-percentile restrictions to disentangle the relationship between uncertainty and real activity, and to sharpen identification of the macroeconomic effects of US monetary policy.

https://www.rba.gov.au/publications/rdp/2026/2026-01/full.html

Abstract: We study the impact of Fed crisis interventions on market fears — the perceived risk of large asset price drops. To do so, we develop a methodological framework that allows us to evaluate the causal effect of unexpected Fed actions on changes in market fears. We extract daily fear term structures from options markets with event horizons ranging from two weeks to ten years. We then use high-frequency price movements around crisis announcements for a wide range of financial assets, including FX, equity, and fixed income markets, to isolate the shock component of Fed interventions. We can measure the heterogeneous effects of various crisis tools by classifying Fed announcement shocks into five different policy groups. Applying this to the market turmoil of 2020, we find that the Fed impacts market fear via risk and information effects. The risk channel dominates at short to medium terms and works via asset purchases, whereas the information channel dominates at longer terms and operates via interest rate policies.

https://www.bankofcanada.ca/2026/05/staff-working-paper-2026-17

Abstract: We evaluate the ability of economic uncertainty measures to forecast recessions in real time. We find that including uncertainty increases the predictive power of both in sample and out-of-sample forecast models relative to a baseline set of financial variables. A nonlinear maximum transformation of uncertainty, which captures whether a measure exceeds its maximum over the past year, improves forecast performance for some measures. Adding a contemporaneous indicator like GDP growth alongside uncertainty yields additional predictive gains. Lastly, ex post Bayesian model averaging outperforms individual uncertainty models and ex ante factors of uncertainty generated using principal component analysis.

https://fedinprint.org/item/fedlwp/103289/original

Abstract: We study the rise of nonbank financial intermediation and its implications for systemic risk. We develop a structural network model of banks and nonbank financial institutions (NBFIs) that decomposes intermediation into a capacity channel, driven by bank balance-sheet constraints, and a reliance channel, reflecting NBFI funding reliance. Using U.S. banking confidential supervisory data, we estimate key structural parameters and quantify both channels. We find that fluctuations in bank-NBFI intermediation are primarily explained by the reliance channel, with variation in NBFI fragility emerging as the dominant driver. We show that NBFI intermediation can amplify shocks through funding interconnectedness.

https://www.federalreserve.gov/econres/feds/bank-regulation-and-the-rise-of-nonbank-intermediation.htm