Using a market-indicator-based approach, this paper empirically examines whether the stability of the US and EU financial systems is affected by the digital finance revolution driven by BigTechs, FinTechs, and crypto-assets. These three sectors display different downside volatility profiles, with financial intermediaries being particularly sensitive to shocks from the crypto ecosystem only under extremely severe downturns, which are prevented in regulated equity markets. In that vein, we provide evidence that the Markets in Crypto Assets Regulation reduced financial systemic risk in EU. Overall, our empirical analysis shows that markets perceive the performance and riskiness of tech-driven companies and assets in differentiated ways, and that the transmission of shocks from digital finance ecosystems operates uniquely under varying conditions of systemic stress. Finally, we also document asymmetric spillover effects between advanced and emerging economies, with shock transmission from the US and EU to emerging markets being systematically stronger than in the reverse direction.

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. 

We compare the effects of external financing shocks on patient mortality at nonprofit and for-profit hospitals. Using confidential patient-level data, we find that patient mortality increases to a lesser extent at nonprofit hospitals than at for-profit ones facing exogenous, negative shocks to debt capacity. Such an effect is not driven by patient characteristics or their choices of hospitals. It is concentrated among patients without private insurance and patients with higher-risk diagnoses. Potential economic mechanisms include nonprofit hospitals' having deeper cash reserves and greater ability to maintain spending on medical staff and equipment, even at the expense of lower profitability. Overall, our evidence suggests that nonprofit organizations can better serve social interests during financially challenging times.

How do beliefs on admission probability influence application choices? In this study, we empirically investigate whether and how admission probability is reflected in application choices in a centralized admission system. We exploit a novel setting of a dynamic deferred acceptance mechanism as employed in Croatia with hourly information updates and simultaneous application choices. This setting allows us to explore within-applicant strategic adjustments as a reaction to changing signals on admission probability. We show in an RDD analysis that applicants react to negative signals on admission probability with an increased propensity to adjust their application choices by 11-23%. Additionally, we show how application strategies evolve over time, while applicants learn about their admission probability. The group most-at-risk to remain unmatched improves their application choices by applying to programs with a higher admission probability towards the application deadline. Yet, we also identify a popular and potentially harmful strategy of applying to safer programs before applying to more risky “reach” programs. About a quarter of applicants have the potential to improve their application choices by resorting their application choices.

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.