The COVID-19 market turmoil triggered significant outflows from euro area money market funds (MMFs), reaching nearly 8% of assets under management. By analysing granular data on derivative contracts and MMF activity, we identify a novel shock transmission mechanism: as financial intermediaries use MMFs as cash management vehicles, derivative margin calls can generate MMF flows. Our findings suggest that margin calls faced by euro area non-bank financial intermediaries drove around 45% of the aggregate outflows from EUR-denominated euro area MMFs between February and April 2020, revealing a critical channel through which market shocks propagate.
This study examines how ambiguity and risk shape green innovation across countries with different institutional environments. Using 163,391 firm-year observations from 35 economies during 1997–2021, we find that risk is positively associated with green innovation among G7 firms, whereas ambiguity shows no statistically significant association on average. Among non-G7 firms, ambiguity is positively associated with green innovation, while risk is negatively associated with it. These associations vary across countries. Our findings suggest that uncertainty may help explain the relationship between institutional environments and green innovation, with implications for policymakers and investors navigating the green transition.
Motivated by growing calls for green-differentiated macroprudential policies (MaPP) and the need to understand the macroeconomic effects of greenwashing, we develop a dynamic general equilibrium framework with green differentiated MaPP, greenwashing, and endogenous growth. Credit-financed entrepreneurs engage in either basic or green variety product development. While the credit for the basic variety development is based on collateralization, green credit is backed by a loan covenant that allows for it to be charged against ex-ante future revenue. Due to the presence of three heterogeneous sources of uncertainty, two novel counteracting channels unique to the transmission of the MaPP-greenwashing nexus to economic growth are identified: the between-variety substitution and the disciplinary-preemptive effects. Empirically, based on a country-year panel of 74 economies (2004–2021), we find support in predicting: (i) conventional MaPP tools (Core LTV and capital adequacy) may discourage greenwashing and support green variety expansion-led growth, though this is largely driven by the between-variety substitution effect; (ii) despite weak empirical support, the concept of a Green LTV may invoke a counteracting disciplinary-preemptive effect in affecting greenwashing incentives; (iii) all three MaPP tools would materially induce economic growth. Further, we also find supporting evidence for both channels as mechanisms in affecting the growth effect transmissions.
Catastrophe (CAT) bonds are an increasingly important instrument for transferring climate-related disaster risk to capital markets. Yet the drivers of their primary market spreads remain incompletely understood. Primary market spreads shape funding costs and the availability of disaster risk transfer at the point of capital formation. Understanding primary market pricing is therefore central to investor decision making and financial market policy assessments. Using a hand-collected dataset of 702 issuances by 101 sponsors (1997–2020), we compare four variable sets containing different types of information across a linear benchmark and nine machine learning (ML) models. We document three main findings. First, selected sponsor and underwriter identities are associated with economically meaningful spread discounts of approximately 0.7–1.7 percentage points. Second, credit market conditions and the reinsurance pricing cycle are significantly associated with primary market spreads and provide the strongest improvements in predictive accuracy. Third, ML methods can materially improve spread predictions, although their relative performance depends on the evaluation setting. Boosted tree models perform particularly strongly in cross-sectional prediction, whereas simpler models generalise better across time. The results allow us to distinguish sponsor- and underwriter-related pricing heterogeneity from the incremental predictive value provided by different variable sets. This has implications for institutions engaged in financial market monitoring and for investors’ capital allocation decisions.
This paper examines the dynamic relationship between real commodity price changes and interest rate changes during the U.S. Federal Reserve’s quantitative easing (QE) and COVID-19-era policies. Employing wavelet coherency and phase difference analysis over the period from January 2003 to December 2025, we provide a time–frequency characterisation of how these variables co-move under unconventional monetary regimes. We find robust short-term positive correlations between interest rate changes and real price changes across metals, energy, agriculture, and food commodities during both the QE and COVID-19 periods. Precious metals exhibit negative correlations during the subprime crisis, reflecting demand for value preservation. Critically, real price changes in hard-to-store commodities frequently lead interest rate changes, consistent with commodity prices anticipating monetary policy actions. To distinguish inflation-related channels from broader confounders, we implement partial wavelet coherency conditioned on money growth and core inflation. The significant attenuation of correlations under these conditions supports the quantity theory of money and the Fisher effect, indicating that the observed co-movements are substantially linked to inflation expectations. Our findings carry a direct policy implication: when the zero lower bound stifles conventional interest rate channels, commodity prices act as high-frequency barometers of evolving inflation expectations. This necessitates that policymakers treat energy, metals, and food markets as primary leading indicators during episodes of unconventional monetary policy. Complementing this policy insight, we introduce a wavelet-based methodology that significantly advances the literature by exposing the nonlinear, time-varying dynamics that standard econometric approaches systematically suppress.
