International Finance最新文献

This study revisits stock market integration in Africa using an information-theoretic framework that quantifies the flow of information between exchanges. We use daily return data for seven MSCI-classified African stock exchanges between 2011 and 2021. As Bitcoin has become an important asset class on the African continent, we also explore whether this cryptocurrency confers any diversification benefits. Our method holds that stock markets are integrated if there is a significant flow of information between exchanges. The results reveal a statistically insignificant flow of information among African stock exchanges, and for the few cases in which information flow is statistically significant, the magnitudes are low. South Africa is the most influential stock market, as it transmits most of the total transfer entropy (informational value) in the system. We also observe that African stock exchanges are weakly integrated with Bitcoin.

Complete financial markets allow countries to share their consumption risks internationally, thereby creating welfare gains through lower volatility of aggregate consumption. Using a panel of 116 countries between 1970 and 2019, I show that a higher share of low-income households reduces consumption risk sharing, especially so in less-developed countries. Moreover, I find that a broad range of financial market reforms and financial integration have a positive impact on international consumption risk sharing in poorer developing countries, while in emerging market countries, financial market development, financial reforms, and capital account openness has an impact. In advanced economies, financial (stock and bond) market development as well as financial integration improves international risk sharing. A lack of financial reforms, a lower degree of financial integration and a high share of low-income households thus contribute to the degree of risk sharing being lower in developing countries than in advanced economies.

As China's carbon market continues to develop, its close connection with the financial and energy markets is becoming increasingly apparent. A systematic study of the spillover effects between markets is important, as it can help prevent excessive fluctuations in carbon prices. With this in mind, this study proposes a time-varying parameter vector autoregression with Lanne–Nyberg decomposition extended joint connectedness approach to analyze quantitatively the spillover effects in the “carbon–energy–financial” system. Empirical results show that a bidirectional spillover effect exists among markets. Not only does the carbon market have the most pronounced return (volatility) linkages with the natural gas (clean energy) market, but the information connected with the energy markets is also more closely linked than with the financial markets. We also find that market fluctuations, caused by the China–US trade conflict and the COVID-19 pandemic, have increased spillovers in the system.

This paper investigates whether and how the development level of a country's digital economy affects stock price synchronicity. The results indicate that countries with high levels of digital economy development exhibit low stock price synchronicity. Additionally, by decomposing stock price synchronicity into systematic and firm-specific stock return variations, we find that systematic (firm-specific) variations of stock returns decrease (increase) with the level of a country's digitalization. These findings shed light on the future trend of stock price synchronicity in financial markets around the world and support the information-based interpretation of stock price synchronicity.

This study examines the role of the global financial cycle (GFCy) in the propagation of uncertainty shocks from the United States to other national economies using a large-scale global vector autoregressive model of 33 countries. Although the dominant role of US uncertainty over global economic dynamics is established, the findings highlight the moderating role of the GFCy in the spillover effects of uncertainty shocks. The US uncertainty shocks, compared with own-domestic uncertainty shocks, are found to have a more prominent negative impact on output during stressed market conditions, implied by low values of the GFCy, while the impact turns largely insignificant during high GFCy states. The findings provide evidence in favour of a US uncertainty spillover multiplier, suggesting that the design of monetary policy as a response to US uncertainty needs to be contingent on the state of the integrated global financial markets, captured by the GFCy.

This study applies the event-analysis method and takes three Chinese listed textile and apparel companies that are representative of the upstream, midstream, and downstream of the textile value chain as research objects. By tracking the Baidu index trend of the keyword “trade war” to identify the ‘time window’ for each iconic event, we apply the autoregressive distributed lag approach to examine the impact of important landmark events on the performance of these companies during the period of Sino–US trade friction in 2018. We find that the impact diminished over time. Additionally, compared with upstream companies, midstream and downstream companies were hurt more. However, the risks were generally controllable.

Currency depreciation dampens corporate investment through a financial channel. Using firm-level data for 16 major economies, we find that depreciation reduces investment by interacting with firm leverage. The finding is consistent with predictions from a stylized model of credit risk in which the exchange rate affects credit supply and investment when firms borrow in foreign currency, or in local currency from foreign lenders. Empirically, the channel is significantly more pronounced in emerging market economies (EMEs), reflecting greater dependence on foreign funding and less developed financial systems. Our findings suggest that the depreciation of EME currencies since 2011 probably contributed in a significant way to the investment slowdown in these economies.

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.