This article investigates five safe-haven asset responses from 2014 to 2022,including the unprecedented COVID-19 crisis,Russian invasion of Ukraine,and sharp US interest rate increases of 2015 and 2022.We apply the un...This article investigates five safe-haven asset responses from 2014 to 2022,including the unprecedented COVID-19 crisis,Russian invasion of Ukraine,and sharp US interest rate increases of 2015 and 2022.We apply the unique approach of the multivariate factor stochastic volatility(MSV)model,which is extremely efficient for financial market analysis and allows us to conduct dynamic factor analysis of safe-haven relationships that cannot be observed directly.The research sample consists of five prospective safe-haven assets—gold,bitcoin,the euro,the Japanese yen,and the Swiss franc—and five primary world stock market indices—the S&P 500,Financial Times Stock Exchange(FTSE)100,DAX,STOXX Europe 600,and Nikkei 225.Our findings are useful for investors searching for the best safe-haven assets among gold,bitcoin,and currencies to hedge against financial turmoil in global stock markets.Our unique findings suggest that safe-haven effects work differently for gold and the yen;that is,the Japanese yen acts as the strongest safe haven across all stock indices.Bitcoin is not a strong safe-haven currency since it has zero days of negative correlations with the considered stock indices,but it is a weak safe-haven during times of financial distress.Consequently,we state that strong and weak safe-haven properties vary across time and place.The novelty of our study lies in the methodological complexity of the MSV model(used for the first time to find the best safe-haven asset properties),dynamic factor analysis,a long-term research sample covering the Russian invasion of Ukraine in 2022,and an international investor perspective focusing on the world’s leading stock markets.We extend earlier studies by analyzing the interrelations of the world’s leading stock market indices with five potential safe-haven assets during the long period of 2014–2022 and using a unique dynamic factor analysis to show the differentiated behaviors of the Japanese yen and gold.Additionally,the main innovative contribution is a new framework of weak and strong safe-haven asset classifications not previously applied in the literature.展开更多
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include...This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.展开更多
The goals of any major business transformation programme in an official statistical agency often include improving data collection efficiency,data processing methodologies and data quality.However,the achievement of s...The goals of any major business transformation programme in an official statistical agency often include improving data collection efficiency,data processing methodologies and data quality.However,the achievement of such improvements may have transitional statistical impacts that could be misinterpreted as real-world changes if they are not measured and handled appropriately.This paper describes a development work that sought to explore the design and analysis of a times-series experiment that measured the statistical impacts that sometimes occur during survey redesigns.The Labour Force Survey(LFS)of the Australian Bureau of Statistics(ABS)was used as a case study.In the present study:(1)A large-scale field experiment was designed and conducted that allowed the outgoing and the incoming surveys to run in parallel for some periods to measure the impacts of any changes to the survey process;and(2)The precision of the impact measurement was continuously improved while the new survey design was being implemented.The state space modelling(SSM)technique was adopted as the main approach,as it provides an efficient impact measurement.This approach enabled sampling error structure to be incorporated in the time-series intervention analysis.The approach was also able to be extended to take advantage of the availability of other related data sources(e.g.,the data obtained from the parallel data collection process)to improve the efficiency and accuracy of the impact measurement.As stated above,the LFS was used as a case study;however,the models and methods developed in this study could be extended to other surveys.展开更多
文摘This article investigates five safe-haven asset responses from 2014 to 2022,including the unprecedented COVID-19 crisis,Russian invasion of Ukraine,and sharp US interest rate increases of 2015 and 2022.We apply the unique approach of the multivariate factor stochastic volatility(MSV)model,which is extremely efficient for financial market analysis and allows us to conduct dynamic factor analysis of safe-haven relationships that cannot be observed directly.The research sample consists of five prospective safe-haven assets—gold,bitcoin,the euro,the Japanese yen,and the Swiss franc—and five primary world stock market indices—the S&P 500,Financial Times Stock Exchange(FTSE)100,DAX,STOXX Europe 600,and Nikkei 225.Our findings are useful for investors searching for the best safe-haven assets among gold,bitcoin,and currencies to hedge against financial turmoil in global stock markets.Our unique findings suggest that safe-haven effects work differently for gold and the yen;that is,the Japanese yen acts as the strongest safe haven across all stock indices.Bitcoin is not a strong safe-haven currency since it has zero days of negative correlations with the considered stock indices,but it is a weak safe-haven during times of financial distress.Consequently,we state that strong and weak safe-haven properties vary across time and place.The novelty of our study lies in the methodological complexity of the MSV model(used for the first time to find the best safe-haven asset properties),dynamic factor analysis,a long-term research sample covering the Russian invasion of Ukraine in 2022,and an international investor perspective focusing on the world’s leading stock markets.We extend earlier studies by analyzing the interrelations of the world’s leading stock market indices with five potential safe-haven assets during the long period of 2014–2022 and using a unique dynamic factor analysis to show the differentiated behaviors of the Japanese yen and gold.Additionally,the main innovative contribution is a new framework of weak and strong safe-haven asset classifications not previously applied in the literature.
文摘This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.
文摘The goals of any major business transformation programme in an official statistical agency often include improving data collection efficiency,data processing methodologies and data quality.However,the achievement of such improvements may have transitional statistical impacts that could be misinterpreted as real-world changes if they are not measured and handled appropriately.This paper describes a development work that sought to explore the design and analysis of a times-series experiment that measured the statistical impacts that sometimes occur during survey redesigns.The Labour Force Survey(LFS)of the Australian Bureau of Statistics(ABS)was used as a case study.In the present study:(1)A large-scale field experiment was designed and conducted that allowed the outgoing and the incoming surveys to run in parallel for some periods to measure the impacts of any changes to the survey process;and(2)The precision of the impact measurement was continuously improved while the new survey design was being implemented.The state space modelling(SSM)technique was adopted as the main approach,as it provides an efficient impact measurement.This approach enabled sampling error structure to be incorporated in the time-series intervention analysis.The approach was also able to be extended to take advantage of the availability of other related data sources(e.g.,the data obtained from the parallel data collection process)to improve the efficiency and accuracy of the impact measurement.As stated above,the LFS was used as a case study;however,the models and methods developed in this study could be extended to other surveys.
