Update from the Chair of Court David Roberts: Sir Ron Kalifa
Update on Sir Ron Kalifa
Update on Sir Ron Kalifa
This paper presents the first causal evidence on how banks adjust their voluntary capital buffers (the capital headroom above the required level) in response to changes in capital requirements. Using granular euro area data and exploiting the threshold-based assignment of Other Systemically Important Institution (O-SII) buffers within a regression discontinuity design, we study the liability side of banks’ balance sheets, complementing the asset-focused literature on lending and risk-taking.
In this paper, we examine how different household consumption items respond to monetary policy shocks in the euro area. Specifically, we classify household consumption along two key dimensions: durability and essentiality. Our findings reveal pronounced heterogeneity in responses across these dimensions. First, durable items are highly sensitive to monetary policy shocks, whereas non-durable items exhibit weaker responses. Second, non-essential items react more strongly than essential items.
Eirik Eylands BrandsaasHousing is the largest asset in U.S. household portfolios, and first-time homebuyers increasingly rely on parental transfers. This paper quantifies the contribution of parental transfers to the homeownership rate of young households. I build and estimate a life-cycle overlapping generations model with housing, where adult children and parents interact without commitment. I find that parental transfers account for 13 percentage points (27%) of young households' homeownership.
Jillian Mascelli and Megan RoddenThis paper analyzes the risks posed by future-state quantum computers, specifically the “harvest now decrypt later” (HNDL) risk. We review foundational concepts of quantum computing to address the present and ongoing threat of HNDL to currently protected data. We use the Bitcoin network as an illustrative example to study the implications of HNDL for distributed ledger cryptocurrency networks that rely upon traditional cryptography.
Results of our latest survey of turnover in the markets for foreign exchange and over-the-counter interest rate derivatives.
Using novel data on sectoral safe asset positions in 21 advanced economies since 1980, we document the central role of the foreign sector in the market for safety and its macroeconomic implications. We show that safe asset holdings have expanded significantly relative to GDP, driven by rising net holdings of the foreign sector and accommodated by increased issuance from the financial and public sectors. Furthermore, fluctuations in safe assets are almost exclusively driven by the foreign and financial sectors, with close links between the two.
We develop a model in which agents face unemployment risk, but also age and eventually retire. We study the impact of different retirement schemes on life-cycle consumption and the monetary transmission mechanism. Agents save because of a fall in income upon retirement, changes along the life-cycle wage profile, and unemployment risk. Changes in retirement policies affect the distribution of available assets (bonds) among the middle aged and the young, which in turn can have a strong impact on the ability of the young to insure themselves against unemployment risk.
Jeffrey S. Allen and Max S. S. HatfieldWe examined the performance of four families of large language models (LLMs) and a variety of common fuzzy matching algorithms in assessing the similarity of names and addresses in a sanctions screening context. On average, across a range of realistic matching thresholds, the LLMs in our study reduced sanctions screening false positives by 92 percent and increased detection rates by 11 percent relative to the best-performing fuzzy matching baseline.
Yesol Huh and Matthew Vanderpool KlingThis paper introduces parallel trends forest, a novel approach to constructing optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to construct an optimal control sample that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data.