Journal of International Money and Finance最新文献

This paper studies the impact of the Eurosystem’s low interest rate policy on euro area banks. We first assess the impact of low rates on banks’ net interest margins. An important extension to previous studies is that we split the interest margin into a funding and lending component. The decomposition makes clear that the low interest rate environment significantly reduced banks’ net interest income by squeezing funding margins, which corroborates the reversal rate literature. We find no strong evidence that banks have boosted their lending margins to offset the lost funding margin. However, we do observe that banks partly compensated for the impact of low rates by switching to non-interest income sources and by cost savings. We also find that low interest rates have not reduced bank lending, which suggests that banks’ compensatory measures largely outweighed the impact of low rates.

This study examines whether a country’s institutional quality can affect its output recovery after the recessions caused by financial crises. Utilizing a sample of 66 countries that experienced various financial crises during the period 1985–2010, we find that the quality of government institutions is negatively associated with the duration of recovery as well as the depth and severity of output losses during recessions. The results remained valid even after accounting for potential endogeneity. Moreover, institutional quality’s ability to improve output recovery is more pronounced for countries with the largest output losses, when coupled with an expansionary monetary policy, in emerging economies, during banking and sovereign debt crises, and in the 1990s.

This paper analyzes the dynamics of sectoral Real Gross Value Added (RGVA) around sudden stops in foreign capital inflows. We identify sudden stop episodes statistically from changes in gross capital inflows from the financial account. In the baseline specification, we estimate changes in the growth rate of sectoral RGVA during sudden stops and in the few quarters preceding and following them. We also look at whether real exchange rate movements and the depth of the RGVA decline on impact explain different sectoral dynamics afterwards. In an additional exercise, we analyze deviations from the sectors' long-run growth path. Our findings indicate that: (i) the construction sector experiences the largest drop in its growth rate during sudden stops; (ii) generally, tradable sectors, especially manufacturing, face larger damages during sudden stops than nontradable sectors, but they decelerate less in the medium run than some service sectors; (iii) the depth of the initial slowdown is related to a more favorable subsequent performance (a rebound effect), while we find only very weak evidence that real exchange rate depreciations facilitate adjustment. Overall, our results suggest a prolonged reallocation of economic activity away from service sectors, towards the production of goods. This is consistent with a traditional view of the role of tradable and nontradable sectors in a sudden stop episode.

This paper measures the output effects of financial fragmentation in the euro area by estimating an extended IS curve. Using a panel approach, we find that two fragmentation measures are significantly related to the output gap: sovereign spreads and spreads in the long-term cost of borrowing of the private sector. We use these output effects to construct a Monetary Conditions Index (MCI) for euro area countries. This index summarizes the combined effect of the monetary policy stance and financial fragmentation. We show that the MCI approach is well-suited to capture cross-country differences in a fragmentation-enhanced measure of the monetary policy stance. Using this metric, we find that during the sovereign debt crisis, the cross-country dispersion of MCI's based on sovereign spreads was much larger than that based on the private cost of borrowing. We also show that convergence is slower for MCI's based on sovereign spreads. We conclude that the causes of fragmentation in monetary conditions may change over time, and that this has implications for the appropriate policy response.

We investigate the behavior of central banks in seven advanced economies, focusing on how observed monetary policies align with optimal ones as determined by model-consistent welfare measures. Our approach stands out by emphasizing the importance of inertia’s impact on the output gap and the dynamics of prices and wages. We incorporate inertia into our model using duration-dependent adjustments. By integrating this aspect into a simple New Keynesian model, our analysis aims to identify shared patterns and distinctive features in the monetary policy approach of central banks across different countries.

We propose the CoJPoD, a novel framework explicitly linking the cross-sectional and cyclical dimensions of systemic risk. In this framework, banking sector distress in the form of the joint probability of default of financial intermediaries (reflecting contagion from both direct and indirect interconnectedness) is conditioned on the financial cycle (reflecting the buildup and unwinding of system-wide balance-sheet leverage). An empirical application to large systemic banks in the euro area, US and UK illustrates how the unraveling of excess leverage can magnify banking sector distress, including during the 2023 US banking sector turmoil. Capturing this dependence of banking sector distress on prevailing financial imbalances can enhance risk surveillance and stress testing alike. An empirical signaling exercise confirms that the CoJPoD outperforms the individual capacity of either its unconditional counterpart or the financial cycle in signaling financial crises – particularly at their onset – suggesting scope to increase the precision with which macroprudential policies are calibrated.

Small open economies are known to be impacted by shocks to larger economies. This phenomenon is known as macroeconomic vulnerability. We propose and implement a novel index of macroeconomic vulnerability to foreign shocks for a given pair of a large economy and a small open economy. It uses a structural time-varying Bayesian VAR with a block-exogeneity hypothesis. The index is based on the sum of the responses of the small open economy to shocks in the large economy over time, thus allowing us to disentangle and measure the source of the shock, the impact variables, and the duration of impact. We highlight two results out of the many that our index unveils. First, we do not find a difference between the international impact of U.S. shocks during periods of crises versus stability. Second, we find that there is a growing decouple between emerging markets (EM) and developed markets (DM) on how their domestic inflation is affected by U.S. output shocks. Our approach can also be used to elucidate previously unknown transmission channels or unmeasured theoretical mechanisms. Finally, using a sample of developed and developing countries, we find that global banks do not increase the macroeconomic vulnerability of a country.

This study proposes a co-bubble network to capture the transmission of co-bubbles across the prices of 37 cryptocurrencies from both static and dynamic perspectives. It considers the periods of the COVID-19 pandemic and the Russo-Ukrainian conflict, and distinguishes clean from dirty cryptocurrencies. The main findings are summarized as follows: Firstly, larger cryptocurrencies, such as Bitcoin, Ethereum, and BNB, have a higher probability of generating co-bubbles in other cryptocurrencies, indicating a strong interdependence among them. Secondly, the co-bubble network experiences notable changes around crisis events, with distinct characteristics observed during the COVID-19 pandemic compared to the Russo-Ukrainian conflict. Thirdly, the transmission of co-bubble influence exhibits time-varying characteristics, and centrality rankings of influential cryptocurrencies vary around the crises. Particularly, after the COVID-19 pandemic, Bitcoin and BNB experience a decline in centrality ranking, while smaller-cap cryptocurrencies show higher centrality rankings, suggesting the transmission of co-bubble effects from large to smaller cryptocurrencies. The centrality rankings of Bitcoin, Ethereum, and BNB show a contrasting pattern, maintaining higher levels in the ongoing post Russo-Ukrainian conflict period. Fourthly, different patterns of co-bubble transmission exist for dirty and clean groups, with dirty cryptocurrencies showing a much higher intensity of co-bubbles during the Russo-Ukrainian conflict. Finally, the portfolio analysis shows that co-bubble network centrality-driven portfolios outperform the baseline portfolio strategy, dirty group portfolio strategy, and clean group portfolio strategy, during the entire sample period and particularly the COVID-19 pandemic. The findings are useful for the decision making of cryptocurrency portfolio managers and policymakers concerned with the behaviour of influential cryptocurrencies and potential risks inferences.

This paper analyzes the causal impact of macroprudential policies on growth, using industry-level data for 89 countries for the period 1990 to 2021. The small industry size creates exogenous identification, avoiding reverse-causality. I find macroprudential tightening measures impact manufacturing growth negatively, but only for industries with high external finance dependence. This effect is stronger during banking crises, higher growth periods and for advanced economies. The effect is weaker during periods of high private credit growth. Prudential policies implemented before the pandemic mitigated the fall in growth. Growth effects on externally dependent industries are economically sizeable and can persist over three years.

Capital flow volatility is a concern for macroeconomic and financial stability. Nonetheless, literature is scarce in this topic. Our paper sheds light on this issue along two dimensions. First, using quarterly data for 33 emerging markets and developing economies, we introduce new estimates of volatility for total multilateral gross capital in- and outflows and key categories, based on the residuals of ARIMA models. We find that a combination of our proposed approach and the commonly used standard deviation best identifies sharp rises during episodes of heightened global risk aversion, thus underscoring the need for a multi-faceted approach to gauge capital flow volatility. Second, we perform panel regressions to understand the determinants of volatility using both ARIMA and standard deviation measures of volatility. While there are variations across different categories of capital flows, generally speaking we identify three main drivers: the US interest rates, global risk aversion, and domestic real GDP. Overall, our findings call for a richer set of volatility estimates, beyond standard deviation, and also show that the determinants of capital flow volatility could vary depending on the measurement approach and the category of flow under analysis.