Exchange of letters between the Governor and the Chancellor on the Asset Purchase Facility - November 2025
Exchange of letters between the Governor and the Chancellor
Exchange of letters between the Governor and the Chancellor
Banks can grant loans to firms bilaterally or in syndicates. We study this choice by combining bilateral loan data with syndicated loan data. We show that loan size alone does not adequately explain syndication. Instead, banks’ ability to manage risks and firm riskiness drive the choice to syndicate. Banks are more likely to syndicate loans if their risk-bearing capacity is low and if screening and monitoring come at a high cost. Syndicated loans are more expensive and more sensitive to loan risk than bilateral loans.
Monetary aggregates provide valuable information about the monetary policy transmission and the business cycle. This paper applies machine learning methods, namely Learning Vector Quantisation (LVQ) and its distinction-sensitive extension (DSLVQ), to identify turning points in euro area M1 and M3. We benchmark performance against the Bry–Boschan algorithm and standard classifiers. Our results show that LVQ detects M1 turning points with only a three-month delay, halving the six-month confirmation lag of Bry–Boschan dating.
Banks can grant loans to firms bilaterally or in syndicates. We study this choice by combining bilateral loan data with syndicated loan data. We show that loan size alone does not adequately explain syndication. Instead, banks’ ability to manage risks and firm riskiness drive the choice to syndicate. Banks are more likely to syndicate loans if their risk-bearing capacity is low and if screening and monitoring come at a high cost. Syndicated loans are more expensive and more sensitive to loan risk than bilateral loans.
Monetary aggregates provide valuable information about the monetary policy transmission and the business cycle. This paper applies machine learning methods, namely Learning Vector Quantisation (LVQ) and its distinction-sensitive extension (DSLVQ), to identify turning points in euro area M1 and M3. We benchmark performance against the Bry–Boschan algorithm and standard classifiers. Our results show that LVQ detects M1 turning points with only a three-month delay, halving the six-month confirmation lag of Bry–Boschan dating.
Debate over China’s growing trade surplus has resurfaced amid US-China trade tensions, geoeconomic shifts and global imbalances. This box shows that the surplus reflects two distinct dynamics: persistently weak imports and surging exports. On the import side, structural policies promoting domestic substitution, trade restrictions and sluggish demand have curbed demand for foreign goods. On the export side, subdued domestic demand has led firms to redirect excess production capacity abroad, consistent with the “vent-for-surplus” mechanism.
The Money Markets Committee is a forum for market participants and authorities to discuss the UK unsecured deposits and funding market and securities lending and repo markets.
In the latest round of the European Commission’s biannual Standard Eurobarometer survey, a record 83% of euro area respondents expressed support for the euro – the highest level since the introduction of the single currency. Using the survey microdata, we show that this rise is broad-based across countries and sociodemographic groups, and that cross-country differences have narrowed significantly.
Collateral reuse in repo markets helps entities meet short-term funding needs, maintain market efficiency, and anchor collateral valuations, although it creates risks through interconnectedness. A prominent view in the literature is that securities dealers use their market position to obtain temporary free-cash wedges from differences in collateral requirements when reusing collateral, so-called “liquidity windfalls”. By affecting dealers’ funding structures, such windfalls could influence yield curve determination, leverage, and monetary policy transmission.
We study heterogeneity in households’ credit across nine European countries (Belgium, Spain, Hungary, Ireland, Italy, Latvia, Lithuania, Portugal, and Slovakia) during 2022-2024 using granular credit register data. We first document substantial between- and within-country variation in mortgage and consumer lending by borrower age, loan maturity, and interest rate fixation. We then quantify the passthrough of the ECB’s recent tightening cycle to household borrowing costs, and assess its heterogeneous impact across households.