This paper investigates the relationship between firms’ participation in global value chains (GVC) and credit risk, measured through delayed payments, overdue obligations, and insolvency risk. We hypothesize that GVC participation is associated with improved firm performance in terms of lower credit risk. To empirically test this hypothesis, we use survey data from the World Bank, combining information on firms’ financial performance during the COVID-19 pandemic with data on their pre-pandemic GVC involvement. The dataset covers more than 8900 firms across 28 countries over the period 2019–2021. We estimate probit and complementary log-log models to assess the association between GVC participation and the probability of experiencing payment delays, overdue obligations, and insolvency risk, while controlling for relevant firm-level characteristics. Our findings indicate that firms with a higher degree of international integration are less likely to face credit repayment difficulties and insolvency risk. We also conduct several robustness checks that support the baseline estimates, although the results should be interpreted as conditional correlations rather than causal effects.
Economic policy uncertainty (EPU) plays an essential role in measuring the long-term component of exchange rate volatility and identifying its long-term risks. This study theoretically analyzes the mechanism through which EPU affects RMB exchange rate volatility. Using Chinese and U.S. EPU information, we employ a GARCH–MIDAS model to examine the explanatory and predictive ability of various EPU indicators for RMB volatility. The results show that the EPUs of China and the U.S., as well as their relative EPU, are positively associated with RMB volatility. Compared with the baseline model, the EPU-augmented GARCH–MIDAS model significantly improves the estimation and better captures the long-term component of RMB volatility, as well as increases the precision of volatility forecasting. In particular, the model incorporating the relative EPU between China and the U.S. yields the best fit and out-of-sample forecasting performance for RMB volatility.
The escalating levels of greenhouse gases, notably carbon dioxide, have led to critical climate change issues, such as global warming, which threatens the survival and development of human beings. This study endeavors to investigate the impact of climate finance (CF) on carbon emission efficiency (CEE) by employing panel data from 108 developing countries for the period spanning from 2008 to 2020. The findings reveal that CF can effectively bolster the CEE of developing countries, and this conclusion has been confirmed through rigorous robustness tests. A deeper analysis of the impact pathways shows that international CF improves the CEE of developing countries via two channels: shifting the industrial structure away from carbon-intensive sectors and raising the share of renewable energy in the energy mix. Furthermore, this impact is more pronounced in countries with lower levels of economic development, non-small island countries, and countries with stronger climate policy efforts.
We propose the embodied real effective exchange rate (EREER), a measure of cost competitiveness at the country-sector level, accounting for global value chains. We derive it from a single Armington-CES framework with intermediate-input linkages. It departs in three respects from conventional REERs, which are predominantly constructed at the national level. First, it is constructed for individual country-sectors, each compared with the same sector abroad; indices for sectoral groups and entire economies follow by aggregation. Second, its deflator is the sector’s embodied unit labor costs, which incorporate labor-cost developments along the entire production chain at home and abroad, rather than in the sector alone. Third, its bilateral weights retain the double-weighting scheme of official REER statistics but are computed from the domestic value added in sectoral sales flows rather than from gross flows. Using global input–output data, we compare the EREER with conventional benchmarks at the sector level for German industries and at the country level for 36 economies. At the sector level, discrepancies are often large, and in some industries the measures even disagree on the direction of change; at the country level, they generally agree on the direction but differ in the magnitude and persistence of cumulative changes. In export regressions for country and sector panels, the estimated responsiveness to effective exchange rates is strongest under the EREER across export outcomes.
We develop a stable derivative-free extended Kalman filter (DF-EKF) for quasi-maximum likelihood estimation (QML) of linear/nonlinear stochastic volatility models. Our study focuses on QML estimation of the weekly exchange rate volatility of South African Rand, the EUR/ZAR and USD/ZAR, from January 2001 to December 2023. Our findings suggest that global financial crisis (2008–2009) and social/political events in South Africa (2015–2016) significantly impacted both exchange rates, leading to highest weekly volatility values. Symmetric SV models provide the most accurate fit for both datasets, suggesting that there is no indication of a leverage effect. A comparative analysis with conditional volatility models like EGARCH and GJR reveals that the USD/ZAR exhibits no leverage, while the EUR/ZAR shows either low or moderate levels of leverage.
