This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existenc...This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.展开更多
Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression...Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression,univariate time series models,and multivariate dynamic time series models.While traditional models likeOLS and ARIMA provided valuable insights,they were limited incapturing the complex temporal dynamics of deforestation.In contrast,the BVAR model demonstrated superior performance by effectively modeling lagged effects and feedback loops amongvariables.Granger causality tests confirmed that cattle ranchingsignificantly precedes changes in deforestation rates.Impulse Response Function(IRF)analysis revealed that shocks in timber extraction,cattle ranching,and soybean production lead to significant increase deforestation,with effects emerging in the 1st,4th,and 8th years,respectively.Variance decomposition showed thatcattle ranching dominates deforestation in the short term,whiletimber extraction becomes the primary driver in the long term.These findings underscore the effectiveness of BVAR in capturing the complexities of land-use change dynamics in the Amazon.展开更多
文摘This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.
基金supported by the Coordination for the Improvement of Higher Education Personnel-Brazil(CAPES)-Financing Code 001,through the Social Demand Program(Programa de Demanda Social-DS)for the doctoral scholarship.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.
文摘Deforestation in the Brazilian Amazon,with approximately 17%ofthe biome lost,remains a critical global issue.This study analyzes deforestation dynamics using various quantitative approaches,including linear regression,univariate time series models,and multivariate dynamic time series models.While traditional models likeOLS and ARIMA provided valuable insights,they were limited incapturing the complex temporal dynamics of deforestation.In contrast,the BVAR model demonstrated superior performance by effectively modeling lagged effects and feedback loops amongvariables.Granger causality tests confirmed that cattle ranchingsignificantly precedes changes in deforestation rates.Impulse Response Function(IRF)analysis revealed that shocks in timber extraction,cattle ranching,and soybean production lead to significant increase deforestation,with effects emerging in the 1st,4th,and 8th years,respectively.Variance decomposition showed thatcattle ranching dominates deforestation in the short term,whiletimber extraction becomes the primary driver in the long term.These findings underscore the effectiveness of BVAR in capturing the complexities of land-use change dynamics in the Amazon.
