On September 25th, financial economists from all over the world travelled for the 11th time to Halle (Saale) to attend the annual FIN-FIRE Workshop at IWH. During two days, authors of ten papers covered a comprehensive overview of contemporary issues that pose potential challenges to the financial system, including data privacy in mortgage markets, climate risks in bond markets, synthetic risk transfers, the effects of geopolitical risks for lending, as well as granular perspectives on the transmission of monetary policy. An intense exchange of thoughts between authors, discussants, and the audience yielded genuinely new insights into the resilience and fragility of financial systems. 

Syndicated loan data provided by DealScan is an essential input in banking research to answer urging questions on bank lending, e.g., in the presence of financial or geopolitical shocks or climate change. However, many data options raise the question of how to choose the estimation sample. We employ a standard regression framework analyzing bank lending during the financial crisis of 2007/08 to study how conventional but varying usages of DealScan affect the estimates. The key finding is that the direction of coefficients remains relatively robust. However, statistical significance depends on the data and sampling choice, and we provide guidelines for applied research.

We exploit staggered real estate transaction tax (RETT) hikes across German states to identify the effect of house price changes on mortgage credit supply. Based on approximately 33 million real estate online listings, we construct a quarterly hedonic house price index (HPI) between 2008:q1 and 2017:q4, which we instrument with state-specific RETT changes to isolate the effect on mortgage credit supply by all local German banks. First, a RETT hike by one percentage point reduces HPI by 1.2%. This effect is driven by listings in rural regions. Second, a 1% contraction of HPI induced by an increase in the RETT leads to a 1.4% decline in mortgage lending. This transmission of fiscal policy to mortgage credit supply is effective across almost the entire bank capitalization distribution.

We examine how banks manage carbon transition risk by selling loans given to polluting borrowers to less regulated shadow banks in securitization markets. Exploiting the election of Donald Trump as an exogenous shock that reduces carbon risk, we find that banks’ securitization decisions are sensitive to borrowers’ carbon footprints. Banks are more likely to securitize brown loans when carbon risk is high but swiftly change to keep these loans on their balance sheets when carbon risk is reduced after Trump’s election. Importantly, securitization enables banks to offer lower interest rates to polluting borrowers but does not affect the supply of green loans. Our findings are more pronounced among domestic banks and banks that do not display green lending preferences. We discuss how securitization can weaken the effectiveness of bank climate policies through reducing banks’ incentives to price carbon risk.

We investigate how global banks’ macroeconomic expectations for borrower countries influence their credit supply. Utilizing granular data on varying expectations among banks lending to the same firm at the same time, combined with an instrumental variable approach, we find that more optimistic GDP growth expectations for a borrower country are strongly linked to increased credit supply. Specifically, a one standard deviation increase in a lender’s GDP growth expectation for the borrower’s country corresponds to an increase of 8.46 percentage points in the loan share, equivalent to approximately 0.75 standard deviations of the loan share and $75.35 million in loan amount. In contrast, global banks’ short-term inflation expectations do not show a significant impact on their credit supply.

We study the corporate-loan pricing decisions of a major, systemic bank during the Greek financial crisis. A unique aspect of our data set is that we observe both the actual interest rate and the “break-even rate” (BE rate) of each loan, as computed by the bank’s own loan-pricing department (in effect, the loan’s marginal cost). We document that low-BE-rate (safer) borrowers are charged significant markups, whereas high-BE-rate (riskier) borrowers are charged smaller and even negative markups. We rationalize this de facto cross-subsidization through the lens of a dynamic model featuring depressed collateral values, impaired capital-market access, and limit pricing.