International Finance最新文献

This study examines the relationship between sentiment and the realized volatility of returns for different asset classes (stocks, bonds, foreign currency, and commodities). Specifically, we aim to answer two key questions: first, how does sentiment relate to volatility during crises (mainly during the global financial crisis [GFC] and the COVID-19 pandemic)? Second, can sentiment be used to forecast volatility during crises? Using two nonparametric methods, mutual information and transfer entropy, we find that information sharing and transfer increased during the pandemic. We also find that sentiment information transfer to the volatility of assets differed between the GFC and the COVID-19 crisis. Since sentiment can reduce uncertainty around the realized variance of assets, we investigate the forecasting ability of sentiment during crises. We find that sentiment has a greater predictive power on realized volatility during crises, with a differential impact on volatility depending on the asset class. Our findings carry important implications for hedging, risk management and building models to predict variance during crises.

Monetary spillovers are heterogeneous in two ways: how central banks generate them and how countries receive them. First, the Fed is mostly unique in its ability to affect other countries' financial markets, among ten developed central banks. This is noteworthy given the lack of data on other central banks' spillovers. This paper makes public a novel data set of these ten central banks' monetary shocks to support future research. Second, the Fed affects recipient countries in different ways, with the bonds and currencies of countries with high-interest rates reacting differently than those of low-rate countries. This can help shed light on theories around the Fed's spillovers, and this paper demonstrates how the exact pattern is inconsistent with models in which developed central banks react to the Fed.

This study sheds light on the finance–growth link by (i) carefully taking into account the lessons learned from the empirical literature, (ii) extending the period of analysis to include the years following the global financial crisis (GFC), (iii) adding the monetary-policy regime as a concomitant factor in this relation, and (iv) running different specifications and following a robust econometric approach. We find that the positive effect of finance via credit vanishes between the end of the 1990s and the beginning of the 2000s, coinciding with most countries reaching a high level of bank credit and with the GFC. This finding is also observed if an inverted U-shaped specification is used to capture the relation between finance and growth. As for the monetary-policy regime, the results reveal that the inflation-targeting strategy does not exert a positive influence on economic growth.

This study investigates what drives the credit cycle, focusing on the role of foreign-funded bank credit (FFC). Considering credit cycles in 41 countries over the period 1985–2015, this study finds that credit booms are associated with an increase in the share of FFC in an economy. This especially holds for emerging economies and for credit provided to nonfinancial corporations. The increased credit needs during a boom may cause the substitution of domestically funded credit by FFC, as the growth in FFC is less restricted than domestically funded credit, such as the domestic deposit base.

This paper examines the role of monetary-policy uncertainty (MPU) in driving business cycles in emerging economies. We employ a Bayesian vector autoregression model with stochastic volatility as the mean for different emerging economies. We find that MPU works as a crucial driver of business cycles in emerging economies. First, we show that an MPU shock can trigger instability in emerging economies by provoking risk/volatility in both financial and exchange-rate markets. Second, an MPU shock can lead to a decline in both output growth and capital inflows in emerging economies. Our empirical results suggest that the central banks of emerging economies should attempt to improve transparency in their monetary policy-making by increasing the effectiveness of public communication and forward guidance.

This study investigates time-varying roles of people's expectations in driving the US housing price and quantity dynamics using a Threshold Vector Autoregressive model. The expectation measure, a good-time-to-buy (GTTB) index, works as the threshold indicator to classify pessimism and optimism phases, and represents the model-based measure of uncertainty. There is strong evidence for regime switches in responses to shocks across the two phases. The results show that good and bad shocks play similar roles in housing markets. Tiny responses of GTTB to both large good and bad shocks in the two regimes suggest “too bad to be believed” and “too good to be believed” patterns. The estimation is biased as volatility shocks are neglected in the housing boom and bust.

This paper investigates inflation convergence among European countries by using sector-level data for the period between 1997:M1 and 2019:M12. Panel unit root tests at the country-sector level are conducted by using moving windows, which are useful for analyzing changes in inflation convergence and the corresponding speed of convergence over time. The results suggest that there is evidence for inflation convergence for the majority of sectors within Europe, although certain countries have experienced disruptions, especially during the 2008 financial crisis. Regarding the speed of inflation convergence, the average half-life across European countries decreased from about 15 months to about 8 months during the sample period. Important sector-level implications follow for European Union (EU) candidate countries and non-euro EU member countries in regard to the Maastricht Treaty.

Is investor overconfidence a major source of stock-market trading volume? This study refers to the work of Grossman and Odean, introduces the assumption of investor overconfidence and empirically examines the influence of investor overconfidence on market trading volume in China's A-share market through a vector autoregressive model estimation and Granger causality test. We find that overconfidence and self-attribution exist in China's A-share market. When the market is on an upswing, investors attribute large returns to the accuracy of their private information and the quality of their investment abilities; thus they trade more frequently, causing trading volume to increase more quickly. Conversely, when the market is on a downswing, investors attribute their investment losses to uncontrollable external factors; thus they become unwilling to trade, causing trading volume to shrink rapidly.

Financial structure, the degree to which a country's financial system is market- or bank-based, matters for economic outcomes. Hence, it is important to understand the drivers of financial structure. This paper sheds light on this issue and explores a specific mechanism in the evolution of financial structure, namely convergence. Financial structure is shown to converge across countries over time. This pattern remains similar even after controlling for macroeconomic and institutional factors, or for banking sector characteristics. It is not specific to a region, and appears to be similarly strong around the globe. However, financial structure convergence is somewhat weaker in the emerging market and developing economies relative to the advanced economies. Given the role of financial structure in economic performance, these findings have implications for macroeconomic and financial policies. Moreover, bridging cross-country gaps in financial structure has potential consequences for income differences across countries.