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Objective: Stratified life expectancy loss by education levels and sex helps measure particular mortality impacts during a catastrophic event. We propose a statistical approach to estimate them using the US case during the COVID-19 pandemic in 2020. Method: First, we estimate life expectancies according to available data, including those years when catastrophic events occur. Second, we use them to calculate a valid multivariate time series VAR(p) model, omitting the respective catastrophic(s) year(s). Through the model, we generate forecasts, which are compared with estimated data, and afterward, the life expectancy losses are quantified as their differences. Results: Less than four times the life expectancy losses with low education compared to the high education group. Our projections also indicate that life expectancies with almost all education falls outside the forecast intervals. Conclusion: The more educated the population is, the less life expectancy is lost. Women always outlive men within each education stratum. Long-term estimates continue to underscore gender disparities in life expectancy.

BackgroundDengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. Therefore, this study examines the effects of meteorological variables (temperature, rainfall, and humidity) on dengue incidence in Bangladesh. While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. Furthermore, in the case of Bangladesh, this type of analysis is necessary due to the fact that dengue outbreak become one of the major issues. However, the analysis related to this issue is not available.MethodsFor estimation purposes, the Augmented Dickey-Fuller (ADF) test, Vector Autoregressive (VAR) model, Granger causality tests, Impulse Response Function (IRF), Variance Decomposition (VDC), and Vector Error Correction Model (VECM) are employed.ResultsRainfall has a significant impact on dengue incidence compared to temperature and humidity. The Granger causality test demonstrates that rainfall and dengue incidence are causally related unidirectionally. Rainfall can potentially have a short-term and long-term effect on the incidence of dengue, as per the estimates of the VECM model.ConclusionsThese findings will assist policymakers in Bangladesh in developing a dengue fever early warning system depending on climate change. In order to efficiently avoid the spread of dengue in Bangladesh's dengue-endemic urban areas, this study suggests societal monitoring.

Purpose – The research identifies the impact of the Russia-Ukraine war on the stock prices of six publicly traded European airlines and evaluates their ability to adapt to this situation. Research methodology – The dependence of the development of airline stock prices on the level of public and institutional stress due to the war was measured using GoogleTrends and is analysed on the basis of a Vector Autoregression model (VAR). Findings – A short-term negative relationship was confirmed between the development of stock prices and GoogleTrends; the impact of the stress related to the war was negligible about 5 months after the outbreak thereof. Those companies that were the fastest to adapt to the shock of the war in terms of share prices are identified. Research limitations – The link between GoogleTrends, as an input variable reflecting market sentiment, and the stock prices of European airlines, is considered a limitation. Practical implications – The impact of investor sentiment on the returns on the stocks of airlines is a thing of the past; which is an important finding for financial market participants and airlines alike. Originality/value – The ability of the specific airlines to adapt to the shock of war creates a competitive advantage.

Brexit, the Covid Pandemic and the Russia-Ukraine War represent the latest three major global events that have demonstrated that financial contagion is a phenomenon that needs careful study because its global effects can cause unprecedented shocks to regional and global financial markets. The main reason is related to the interconnectedness of these markets, the interdependence between countries, and the connections created over decades between national and international financial institutions. In this paper, we aim to analyze, using the Diebold-Yilmaz (DY) methodology proposed by Diebold and Yilmaz in 2014, the effects of financial contagion in the three major crises Brexit, Covid and the Russia-Ukraine war (first year). Financial contagion is primarily a fear-driven phenomenon. Financial network connectivity has the potential to change due to investors’ fear during events that are disturbing and cause exogenous turbulence. We use the network analysis established by Diebold and Yilmaz (2014) to study how SCDS (Credit Default Sovereign Markets) markets changed their interconnectedness around exogenous shocks in the last decade (Brexit, the Covid-19 pandemic, and the Russian invasion of Ukraine).

Over the past few years, many research papers have referred to stock market volatility in relation to investor attention and sentiment and our article adds to the current literature on financial market reactions to the economic consequences of COVID-19. An event such as an outbreak of an infectious disease causes a negative change in investor sentiment, which strongly influences their investment decisions and, consequently, stock market prices. The subject of the study is the mutual influence of stock market characteristics and market sentiment, during a COVID-19 pandemic crisis. The purpose of the study is to provide empirical support for the hypothesis of indirect impact of uncertainty and panic under the COVID-19 pandemic on the dynamics of the stock market in Russia. The World Health Organization and experts forecast that the world will face more than one crisis related to the spread of infectious diseases in the future, so understanding the mechanisms of mutual influence of sentiment and financial markets remains relevant. In this study, we take a novel approach to deriving an indicator for panic that has not been used before. We perform econometric modeling using a Vector Autoregressive Model (VAR) and a Vector Error Correction Model (VECM), which allows us to describe in the model not only the long-term equilibrium but also the dynamics towards it. As a result, we got consistent and efficient estimates of the long-term and short-term effects of panic and mortality rates on the volatility of the RTS stock index and found that the market reaction to COVID-19 changed as the pandemic spread: the effects of uncertainty and panic, while having a significant impact at the beginning of the crisis, faded away. The conclusions obtained in the analysis of the Russian stock market dynamics coincide with those obtained by other authors in their analysis of markets in other countries over a similar period.

Abstract The Suez Canal (SC) serves as the shortest maritime transport route from east to west. In the absence of the SC, global trade and transportation costs would increase substantially, impeding the expansion potential of the global economy. The Belt and Road Initiative (BRI) is a key component of China's future international trading network, with significant implications for global seaborne trade. The BRI's two primary pillars are the Maritime Silk Road (MSR) and the Silk Road Economic Belt, both of which have significant infrastructural investments. The MSR connects China to various regions in Asia, Africa, and Europe via the SC, thereby serving as a significant maritime trade route on a global scale, particularly between Europe and China. This is due to the SC's distinctive positioning on the MSR. Consequently, studying the significance of BRI for the SC cargo trade is crucial. This study uses annual data from 1990 to 2022 to examine this dynamic relationship. To account for the interaction effect of the variables, we use the vector autoregressive model and the impulse response function. Model results show that China's seaborne trade will increase SC trade by 23%, and China's BRI investment projects are anticipated to have a 5% significant impact on SC cargo trade and will continue to grow in the medium and long run. According to these findings, the SC has to continue to adopt more flexible pricing and marketing strategies to encourage and attract more customers. As a result, the SC could become a global logistics center and transform from a trade gateway to a global hub if it develops more value-added activities in its adjacent areas and attracts substantial Chinese investments.

In this research, modeling and estimation of dew point temperature values ​​in eight meteorological stations located in the eastern regions of Iran were done. These stations, including Bam, Birjand, Iranshahr, Kerman, Mashhad, Tabas, Zabol and Zahan, are all characterized by a dry climate. First, the correlation of different weather parameters with dew point temperature was investigated and then the parameters of mean temperature, maximum temperature and minimum temperature were selected as the parameters with the highest correlation to dew point temperature. These selected parameters then incorporated into a VAR (Vector Autoregression) model as inputs for estimating dew point temperature values. This modeling approach allows us to capture the interdependencies between these variables and enhance our accuracy in predicting dew point temperature. Then the stability of the residual series of the VAR model was investigated and the residual series of this model was developed using the generalized ARCH model. The result of the development of the VAR model was the investigation of the dew point temperature in eight meteorological stations with the VAR-GARCH model. The results indicated that this combined model outperformed VAR model in both the train and test phases. Specifically, the VAR-GARCH model demonstrated higher accuracy and improved results compared to solely using a VAR model. The incorporation of GARCH allowed better modeling of the residual series, leading to an overall increase in accuracy ranging from 5% to 30% during the test phase. These findings suggest that considering both autoregressive dynamics and conditional heteroskedasticity is crucial for accurately predicting dew point temperatures. By incorporating GARCH into our modeling approach, we were able to capture additional information about volatility and further enhance our predictions.

In 2005, the Fifth Plenary Session of the 16th Central Committee of the Communist Party of China introduced a strategic plan to advance the construction of a new socialist countryside, thereby providing a policy foundation for the robust development of rural tourism. Against this policy backdrop, the present study investigates the impact of rural tourism policies on the spatial evolution of ethnic minority villages in Yanbian Prefecture, utilizing data from the period 2004–2023. As a representative region in China where ethnic minorities coexist, Yanbian Prefecture exhibits distinctive cultural and spatial features in its Korean villages, making it a key pilot area for rural tourism development. This study utilizes the PMC index model, the coupled coordination degree model, and the vector autoregressive model to analyze the implementation effects of rural tourism policies and to establish an index system for rural spatial construction. By examining the spatial evolution of representative ethnic minority villages in Yanbian Prefecture, the research explores the dynamic interactions between tourism policies and rural construction, as well as the underlying causal mechanisms. The findings indicate that: (1) in ethnic minority villages, geographic characteristics and various constraints contribute to delayed initial policy effects, with negative fluctuations observed, highlighting a distinct lag effect in the policy implementation process; and (2) a significant Granger causality exists between tourism policies and rural spatial construction, with varying effects observed across different dimensions. The study centers on ethnic minority settlements, systematically analyzing the dynamic effects of tourism policies in the context of their spatial evolution characteristics. It offers sustainable development policy recommendations tailored to the unique attributes of ethnic minority villages. lt is suggested that the actual needs of village construction and long-term development goals should be fully considered when formulating and implementing policies to promote the sustainable development of ethnic minority areas.

Macroeconomic uncertainty is one of the important factors affecting the price of the mutton sheep industry chain. Using monthly data on the price of the mutton sheep industry chain from June 2011 to June 2022 as the research sample, the VAR model is used to test the impact of macroeconomic uncertainty on the price of the mutton sheep industry chain through impulse response function and variance decomposition. The results indicate that macroeconomic uncertainty helps stabilize the prices of the mutton sheep industry chain. The response period of macroeconomic uncertainty to the price impact of the mutton sheep industry chain is two years, and the contribution rate of macroeconomic uncertainty to the mutton sheep industry chain price in the second year is about 6.924 5%. Based on this, countermeasures and suggestions are proposed to improve the circulation mechanism of the mutton sheep market, establish a price monitoring system for the mutton sheep industry chain, and promote moderate scale mutton sheep breeding, in order to promote the sustainable development of the mutton sheep industry. [ABSTRACT FROM AUTHOR]

Water scarcity has significantly hampered China's economic, social, and environmental development. Ensuring sustainable water utilization is crucial given the mounting water stress accompanying continuous economic growth. A quantitative water resource forewarning model was constructed using the vector autoregressive (VAR) model. By analyzing the key indicators related to water systems and GDP data from 2001 to 2022, the VAR model revealed the long-term dynamic correlation between water consumption and economic growth using generalized impulse response, co-integration, and predictive variance decomposition analyses. The results revealed the presence of a long-term equilibrium between water consumption and economic growth, with a stable co-integration relationship and an optimal lag period of one year. The positive impact of water consumption on economic development increased during the 2001–2022 period, indicating a rising dependence of GDP on water resources. Water usage rose with economic development, while the water resource carrying capacity remained high and continued to grow. Based on the generalized impulse response, co-integration, and predictive variance decomposition analyses, this study predicted water-use-related indicators, providing vital early warnings for China's water environment carrying capacity from 2023 to 2050. This enabled informed decision-making and fostered sustainable water management practices for the future. [ABSTRACT FROM AUTHOR]

In order to ensure the reasonable and stable price of beef and promote the sustainable and healthy development of the beef market, based on the monthly data of Chinese livestock industry from December 2011 to December 2022, the paper selects corn price, producer expectation, calf price, and national policy to build a VAR model from the perspective of internal transmission and external impact, and empirically tests the influencing factors and degree of beef price fluctuation in China. The results show that corn prices, producer expectations, calf prices, and national policies can affect beef prices. Among them, the corn price has a significant impact on the fluctuation of beef price through the internal transmission mechanism, and the stable contribution rate is about 8%. National policy has a significant impact on the fluctuation of beef prices in China through the external impact mechanism,with a contribution rate of 15%. The price of beef in the early stage significantly affects the market price of beef in the later stage. Therefore, some suggestions were put forward to reduce the feed cost of the beef cattle industry, strengthen the financial support of the beef cattle industry, and improve the market supervision system of beef price fluctuation, in order to provide a reference for stabilizing the beef price and promoting the sustainable development of the livestock industry. [ABSTRACT FROM AUTHOR]

The Romanian manufacturing industry was significantly impacted by COVID-19 pandemic leading to an output contraction. Global value chain disruptions and fluctuating domestic and foreign demand were contributing factors. The war in Ukraine further harmed the economy, especially the manufacturing industry. The inflationary shock generated by the economic and geopolitical consequences of the war impacted the cost structure of manufacturing industry, affecting its competitiveness. The present paper presents three scenarios for Romania's manufacturing industry from 2023 to 2026, considering key macroeconomic variables like GDP and exports. The baseline scenario suggests a limited contribution of the manufacturing industry to GDP growth in the short and medium term. Moreover, if the war in Ukraine has a stronger impact on the economy, the manufacturing industry output may experience a reduction of approximately 12.3 percent compared to the baseline scenario by 20226 [ABSTRACT FROM AUTHOR]

The war in Ukraine had a notable impact on the Romanian economy and the broader European economy, in which Romania is strongly integrated. The inflationary shock generated by the economic and geopolitical consequences of the war in Ukraine impacted the cost structure of industry, affecting its competitiveness. The manufacturing industry remains a crucial driver of economic development, as evidenced by the strong correlation between manufacturing production dynamics and gross domestic product growth. In light of these circumstances, the present study focuses on examining the structural changes within the manufacturing industry and identifying the activities that are particularly susceptible to energy shocks based on energy consumption and efficiency. To investigate the impact of energy price shocks on the most vulnerable sectors of the manufacturing industry, a VAR model is employed. The findings indicate that the energy intensive sectors where negatively affected by the price shock except for Manufacture of coke and refined petroleum products which benefited directly from the energy price rises. [ABSTRACT FROM AUTHOR]

The improvement of the energy structure and the development of the renewable energy sector play an important and strategic role in Cameroon's responsibilities against climate change. So, the authorities and policy makers have made great efforts to achieve this. But one of the main constraints of Cameroon's energy transition lies in financial issues, which are inevitably linked to the country's financial development. At the heart of this reflection therefore arises the question of knowing: What could be the impact of financial development on the consumption of renewable energies in Cameroon? This study therefore aims to examine the impact of financial development on the consumption of renewable energies in Cameroon over the period from 2000 to 2022. Our research is based on a Vector Autoregressive (VAR) model; seven variables of financial development have been used to explain the consumption of renewable energies in Cameroon. The results of the empirical analysis show that foreign direct investments, the evolution of bond markets and energy prices represented by the consumer price have a negative effect on the consumption of renewable energies. On the other hand, domestic credit provided by the financial sector, banking availability, economic development, and income have a positive impact on renewable energy consumption. Our empirical results provide valuable insights into the best ways to deploy capital in the renewable energy sector, in order to provide customers with cost-competitive options, and to facilitate the implementation of policies that contribute to environmental sustainability and energy security. [ABSTRACT FROM AUTHOR]

Based on the monthly data of 132 fattening feed prices for mutton sheep from January 2012 to December 2022, this paper selects soybean meal price, wheat bran price, mutton price, and natural disaster risk index as explanatory variables to analyze the influencing factors of fattening feed price fluctuation for mutton sheep in China. The results showed that the price of soybean meal had a significant impact on fattening feed of mutton sheep through an internal transmission mechanism. Natural disasters had obvious influence on the fluctuation of fattening feed price of mutton sheep through external impact mechanism. The fattening feed price of early mutton sheep had a significant impact on the fattening feed price of late mutton sheep. The study indicates that the conclusion of this paper can provide a reference for maintaining the long-term stability of fattening feed price of mutton sheep. [ABSTRACT FROM AUTHOR]

Strengthening the supply of feed grain is an important starting point for stabilizing the supply and demand of feed grain market and promoting the high-quality development of animal husbandry. Based on the monthly data samples of feed grain supply and demand structure from January 2006 to December 2022, this paper uses the VAR model to empirically analyze the feed grain supply and demand structure in China. The results showed that maize supply had the most obvious influence on feed grain supply and demand structure, followed by urban and rural residents' consumption of livestock products, and rice supply was the lowest. From the perspective of supply, the main influencing factors of feed grain supply and demand structure change include maize supply, soybean meal supply, sorghum supply, and wheat supply. From the perspective of demand, the consumption of livestock products by urban and rural residents was the main factor affecting the change of feed grain supply and demand structure. From the perspective of emergencies, natural disaster risk index was not the Granger cause of the change of feed grain supply and demand structure. Under ignoring other factors, the contribution rate of natural disaster risk index to the change of feed grain supply and demand structure after seven weeks was 23.62%. Research shows that changes in the supply and demand structure of feed grains are mainly influenced by factors such as corn supply, consumption of livestock products by urban and rural residents, soybean meal supply, sorghum supply, wheat supply, and natural disaster risks. [ABSTRACT FROM AUTHOR]

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area, China. Based on panel data spanning 2011–2020, a vector autoregressive (VAR) model is used to analyze and forecast the short-run and long-run relationships between three industrial structures, pollutant discharge, and economic development. The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index. Per capital chemical oxygen demand (PCOD) and per capita ammonia nitrogen (PNH3N) had a positive impact on delta per capita GDP (dPGDP), while per capita solid waste (PSW), the secondary industry rate (SIR) and delta tertiary industry (dTIR) had a negative impact on dPGDP. The VAR model under this coupling system had stability and credibility. The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results. In addition, variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods (i.e., ten years). It was found that dTIR had a great impact on dPGDP, with a contribution rate as high as 74.35% in the tenth period, followed by the contribution rate of PCOD up to 3.94%, while the long-term contribution rates of PSW, SIR and PNH3N were all less than 1%. The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration. [ABSTRACT FROM AUTHOR]

The aim of the article is to improve the methodology of studying the financial market of Ukraine, analyzing the interaction between the components of the financial market, studying the reaction of the components of the financial market to sudden changes and forecasting the behavior of the structural elements of the financial market. The financial market plays a decisive role in the modern market economy of Ukraine, providing a mechanism for the redistribution of capital between creditors and investors through intermediaries based on the principles of supply and demand. The development of the financial market and the interaction of its components is the subject of research by many scholars, whose works laid the foundation for a modern understanding of how financial markets function and how they affect the economy as a whole. Despite a significant number of studies on the functioning and development of financial market infrastructure, many aspects of the interaction of market entities in Ukraine remain unexplored, especially in changing external and internal conditions. To model the interaction of market participants in the financial market of Ukraine, an algorithm has been developed, on the basis of which the interaction of structural elements of the financial market of Ukraine has been analyzed, the reaction of the components of the financial market to sharp changes has been studied, and forecasts of the behavior of its structural elements have been made. The study uses a combination of fundamental conceptions of the financial market and the methodology of expert evaluation to determine the most significant statistical indicators that characterize its constituent components. The cause-and-effect relationships between these components are analyzed, the nature of their mutual relationship is revealed. ARIMA models were used to forecast key indicators of the financial market. On the basis of vector auto-regressive models, the dynamic adaptation of market components to shocks is analyzed. The study showed that the credit and interbank markets show less susceptibility to external factors and can even serve as impetus for changes in other structural elements of the financial market. The results of the study can be used for: development of a more effective policy of financial market regulation; development of new methods and tools of influence on the functioning of financial market entities. [ABSTRACT FROM AUTHOR]

The Suez Canal (SC) serves as the shortest maritime transport route from east to west. In the absence of the SC, global trade and transportation costs would increase substantially, impeding the expansion potential of the global economy. The Belt and Road Initiative (BRI) is a key component of China's future international trading network, with significant implications for global seaborne trade. The BRI's two primary pillars are the Maritime Silk Road (MSR) and the Silk Road Economic Belt, both of which have significant infrastructural investments. The MSR connects China to various regions in Asia, Africa, and Europe via the SC, thereby serving as a significant maritime trade route on a global scale, particularly between Europe and China. This is due to the SC's distinctive positioning on the MSR. Consequently, studying the significance of BRI for the SC cargo trade is crucial. This study uses annual data from 1990 to 2022 to examine this dynamic relationship. To account for the interaction effect of the variables, we use the vector autoregressive model and the impulse response function. Model results show that China's seaborne trade will increase SC trade by 23%, and China's BRI investment projects are anticipated to have a 5% significant impact on SC cargo trade and will continue to grow in the medium and long run. According to these findings, the SC has to continue to adopt more flexible pricing and marketing strategies to encourage and attract more customers. As a result, the SC could become a global logistics center and transform from a trade gateway to a global hub if it develops more value-added activities in its adjacent areas and attracts substantial Chinese investments. [ABSTRACT FROM AUTHOR]

Because options on 10-year Japanese government bond (JGB) futures are relatively new in the market, their implied volatility, JGB-VIX, is not available before 2007. For the period when JGB-VIX is available, we conduct supervised learning by using the daily newspaper articles as input and JGB-VIX as output. We then construct a new JGB market uncertainty measure, which we called JGB-NU, based on the predicted values of JGB-VIX from the estimated model and contents of the newspaper articles from 1981 to 2021. In the VAR analysis with JGB-NU, we confirm that JGB market uncertainty shocks have a negative impact on real economic activities in Japan. [ABSTRACT FROM AUTHOR]

The drastic price fluctuation of pig industry chain is not only related to the vital interests of consumers, but also has an important impact on the long-term development of pig industry. Based on 161 monthly sample data from 2009 to 2022, using the VAR model, this paper explores the influencing factors of price fluctuation of pig industry chain from two levels: The main product prices of pig industry chain, upstream link and middle-down stream link. The research shows that the price fluctuation of pig industry chain is most affected by the prices of its main products, and the prices of main products in the first 10 periods still have 61.835 8% influence on the price fluctuation of pig industry chain in the current month. From the upstream link of industrial chain, rice bran price, soybean price and wheat bran price can all have a significant impact on the price fluctuation of pig industrial chain, and the impact of rice bran price in the first 10 periods on the price fluctuation of pig industrial chain in the current month is 39.783 8%. From the middle-down stream link of the industrial chain, inflation level and farm scale have a great influence on the price fluctuation of pig industrial chain. Research has shown that the price fluctuations in the pig industry chain are easily influenced by factors such as the prices of its main products, rice bran, soybeans, wheat bran, inflation levels, and farm scale. Therefore, efforts should be made to improve the price fluctuation warning mechanism and enhance the regulation of circulation and operation links to promote the stable development of the pig market. [ABSTRACT FROM AUTHOR]

Maintaining a rural-urban balance is absolutely necessary in the context of the challenges related to food security and environmental protection as well as the perspectives of urban agglomeration. This paper highlighted the impact of the main events from the last 30 years on the rural population. This study used the vector autoregressive model (VAR) to investigate the estimates and analyse the dynamic impact of innovations on the system of variables. Key issues that influenced certain periods in the analyzed interval were highlighted, which clearly show the impact on the analyzed variables using variance decomposition. The cost of living in rural areas is increasing, so more and more inhabitants give up growing certain crops or raising certain animals, thus having to search for food to survive, leading to additional wage income to cover these costs. [ABSTRACT FROM AUTHOR]

In the paper, we explore the effects of monetary policy shock on the economic growth in systemically important countries such as the US, the Euro Area and China and their impact on Ukraine. Thus, the war in Ukraine and the rise of key policy rates by central banks to curb inflation have had a significant negative impact on economic activity. There has been both a significant decrease in trade activity and a slowdown in the services sector growth, which was the main engine of global economic growth at the beginning of 2023. Based on the vector autoregression model results, we demonstrated a slight initial decline of GDP growth with following stabilization in response to the rise of key policy rates in the US. In China, this decline is much bigger and constitutes 11% after the shock. In the Euro Area, we also observe a similar pattern as in the US with an initial decline up to 2% and a further return to equilibrium. Analyzing the results of the forecast error variance decomposition, we should note that GDP fluctuations in systemically significant economies are mainly explained by their own fluctuations. The key policy rate’s contribution ranges from 1% in the US to 11% in China. At the same time, economic growth in China is less vulnerable to inflation fluctuations, in the Eurozone we observe the most sustained one among the considered economies. In addition, the study shows that the key policy rate in the United States has a positive effect on the one in Ukraine. [ABSTRACT FROM AUTHOR]

The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising capital reserve strategies. Nonetheless, the predictive precision of VaR models frequently faces scrutiny, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability of VaR approaches. This study critically evaluates the efficacy of GARCH-type VaR models within the transportation sector amidst the Thai stock market's volatility during the COVID-19 pandemic. The dataset encompasses daily price fluctuations in the Transportation Sector index (TRANS), the Service Industry index (SERVICE), and 17 pertinent stocks within the Stock Exchange of Thailand, spanning from 28 December 2018 to 28 December 2023, thereby encapsulating the pandemic era. The employed GARCH-type VaR models include GARCH (1,1) VaR, ARMA (1,1)—GARCH (1,1) VaR, GARCH (1,1)—M VaR, IGARCH (1,1) VaR, EWMA VaR, and csGARCH (1,1) VaR. These are juxtaposed with more traditional, less computationally intensive models like the Historical Simulation VaR and Delta Normal VaR. The backtesting methodologies encompass Kupiec's POF test, the Independence Test, and Christoffersen's Interval Forecast test. Intriguingly, the findings reveal that the Historical Simulation VaR model surpasses GARCH-type VaR models in failure rate accuracy. Within the GARCH-type category, the EWMA VaR model exhibited superior failure rate accuracy. The csGARCH (1,1) VaR and EWMA VaR models emerged as notably robust. These findings bear significant implications for managerial decision-making in financial risk management. [ABSTRACT FROM AUTHOR]

Based on the data from the 1991–2016 agricultural investment of China's Taiwan in China's Mainland and the agricultural GDP of the latter, through models of vector autoregressive (VAR) and vector error correction (VEC), the influences of China's Taiwan agricultural investment on the development of agriculture in the eastern, central, and western regions of China are discussed. The results show a long-term equilibrium relationship between China's Taiwan agricultural investment and agricultural development in China's eastern, central, and western regions. In the long term, China's Taiwan investment in agriculture in the eastern, central, and western regions of China have certain positive promoting effect on their agricultural development. However, there is an obvious regional diversity in investment effect: Impulse response and variance decomposition show that the positive effect from China's Taiwan agricultural investment in China's western region agricultural development is most significant, and it is significantly higher than that in the eastern region; its contribution to the central region's agricultural development is little. VEC model analysis shows that in the short term, China's Taiwan investment in agriculture has a significant positive effect on the agricultural development of China's eastern region, but not on the agricultural development of the central and western regions. [ABSTRACT FROM AUTHOR]

The abundant and numerous uses of steel in various industries have turned it into a strategic commodity, and its price has always been a concern of industrial owners. Therefore, access to accurate forecasts of the price trend of steel and its products is important. Therefore, in the present study, while identifying the determinant variables for the price of steel, an out-of-sample forecast for 2023:10 to 2024:06 has been made using the vector autoregression (VAR) model. At first, the results of the Johansen-Juselius cointegration test confirmed the long-term relationship. Also, the error correction coefficient (ECM) was -0.0842. We analyze impulse response functions. The unofficial exchange rate, and industrial producer price index (base metal manufacturing sub-group) (respectively with a positive effect of 6.8% and 6.5% percent in the standard form) have been more effective than other model variables on the fluctuations of steel price. In addition, the results of variance decomposition showed that the industrial producer price index (16.18%), and unofficial exchange rate (11.7%) after the price of steel itself had more effect on the price fluctuations of this product than other variables. Finally, we estimate the out-of-sample forecast. The price of steel is forecasted from 302,445 Rials in October 2023 to 321,552 Rials in June 2024 (with a 5% increase). Based on the forecast evaluation criteria, our model can accurately forecast the price trend of steel. According to the results, the most important policy recommendations include updating the technologies, equipment, and machinery used in the production of steel products to develop the production of steel products, saving energy consumption and production costs, adopting appropriate monetary and foreign exchange policies, And increasing exports by focusing on marketing and sales of steel products (to prevent raw sales), through the development of production, increasing the variety, quality, and durability of manufactured steel products can be provided

The determination of prices for goods and services in a specific economy is significantly influenced by commodity prices and the distribution of energy. Hence, this study aims to examine the impact of fuel prices and electricity supply on the inflation rate in South Africa. The empirical analysis focuses on a time series data covering the period from January 2009 to December 2021. The study utilizes the Johansen test for cointegration, Vector Error Correction Model (VECM), and Granger approaches to establish the relationship between the variables. The empirical findings indicate the existence of a long-term relationship among the variables, indicating that long-term changes in electricity supply and fuel prices have an influence on South Africa's inflation levels. Similarly, the short-term results suggest an inverse relationship between fuel prices, electricity supply, and the inflation rate. In order to reduce the inflation rate, it is recommended to decrease spending on imported fuel and enhance electricity supply through increased investment in renewable energy and research and development (R and D).

Knowledge of the causality between Energy Consumption and GDP is important because it leads to the future actions of policymakers, such as developing infrastructure in case GDP growth depends on Energy Consumption. Hence, the Granger Causality between the Gross Domestic Product and Energy Consumption of Ecuador is analysed in this research. For this purpose, a VAR model was developed with data from 1965 to 2022. Before including the time series inside the VAR model, ARIMA models were evaluated so that the need for differentiation and the use of dummy variables was detected. To ensure that the models include all possible information from the available data, the residuals were diagnosed until they did not have any autocorrelation between each other, there was no evidence of heteroskedasticity, and the residuals had a normal distribution. The Akaike Information Criteria and the Schwarz criteria indexes were compared to detect causality. The Granger p-value was also used to detect the probability of having null coefficients in the added time series. In the end, it was shown that Energy Consumption Granger causes Gross Domestic Product growth, but the same does not happen in the reverse direction. As a consequence, the government could support the development of energy infrastructure to incentivise economic growth.

The improvement of the energy structure and the development of the renewable energy sector play an important and strategic role in Cameroon's responsibilities against climate change. So, the authorities and policy makers have made great efforts to achieve this. But one of the main constraints of Cameroon's energy transition lies in financial issues, which are inevitably linked to the country's financial development. At the heart of this reflection therefore arises the question of knowing: What could be the impact of financial development on the consumption of renewable energies in Cameroon? This study therefore aims to examine the impact of financial development on the consumption of renewable energies in Cameroon over the period from 2000 to 2022. Our research is based on a Vector Autoregressive (VAR) model; seven variables of financial development have been used to explain the consumption of renewable energies in Cameroon. The results of the empirical analysis show that foreign direct investments, the evolution of bond markets and energy prices represented by the consumer price have a negative effect on the consumption of renewable energies. On the other hand, domestic credit provided by the financial sector, banking availability, economic development, and income have a positive impact on renewable energy consumption. Our empirical results provide valuable insights into the best ways to deploy capital in the renewable energy sector, in order to provide customers with cost-competitive options, and to facilitate the implementation of policies that contribute to environmental sustainability and energy security.

Cet article examine le rôle des chocs liés aux catastrophes dans un modèle d’équilibre général dynamique stochastique (DSGE). Dans un premier temps, nous estimons un modèle vectoriel autorégressif (VAR) en données de panel pour la production, l’investissement, la balance commerciale, la consommation et le risque pays afin de capturer les effets économiques de la production, du risque pays et des chocs exogènes liés aux chocs de catastrophes naturelles.Ensuite, en tenant compte des événements rares et des chocs de productivité tendanciels, nous proposons également un cadre de référence des interactions dynamiques entre les effets macroéconomiques des événements rares et les frictions financières pour deux pays : la Barbade et le Belize. Les résultats convergents entre le cadre empirique et le cadre général démontrent que les chocs dus aux catastrophes naturelles en Amérique centrale et dans les Caraïbes n’ont un impact significatif qu’à court terme. Ces résultats indiquent que les pays des Caraïbes manifestent une plus grande résilience face aux chocs liés aux catastrophes naturelles.

This study mainly focuses on the reasonable determination of the stock pledge rate in the A-share market. Based on the Value-at-Risk (VaR) and modified Expected Shortfall (ES) models, 137 sample stocks were used to measure the pledge rate of stocks. The breakdown times and breakdown probability were used to verify the validity of the two models. Firstly, the results indicate that the adjusted breakdown probability calculated by the two models is less than 5%, the two models are highly reliable in measuring the stock pledge rate. Secondly, the stock pledge rate measured by the modified ES model is more effective compared with the VaR model. Finally, the prediction results of modified ES are still robust under different warning lines and liquidation lines. [ABSTRACT FROM AUTHOR]

This study investigates the transmission of risk shocks between China’s stock and currency markets with global commodities (including crude oil, natural gas, gold, silver, copper, palladium, and platinum) over time and across different frequencies, while accounting for the role of China’s economic policy uncertainty (EPU), from January 1, 2016, to June 30, 2022. Our findings reveal that both the time and frequency domain total connectedness index varies over time and suggest that both China’s stock and currency markets can provide more hedge advantage for turmoil periods. Return shocks between global commodities and China’s stock and currency markets have short-lasting effects when considering the frequency domain connectedness analysis. These return shocks typically originate from China’s stock market and currency and transmit to the commodity market, except for natural gas. Additionally, an increase in China’s EPU indicates bad news for the overall connectedness of all considered markets and the gold and silver markets. [ABSTRACT FROM AUTHOR]

Sri Lanka's monetary policy has evolved differently during and after a three-decade-long ethnic conflict. This paper empirically investigates effects of monetary policy shocks on Sri Lankan economy with particular focus on the strength of credit and exchange rate channels using a VAR model for 2003–2019 period. We find that: (i) monetary policy shocks have a significant and persistent impact on key macroeconomic variables even though several puzzling results emerge; (ii) the effects of monetary policy shocks are significant and more persistent in the post-conflict period than in the conflict period; (iii) a tight monetary policy effectively contains inflation in the post-conflict period; and (iv) interest rate and exchange rate channels play a dominant role while the credit channel responds with some lags in the post-conflict period. [ABSTRACT FROM AUTHOR]

⑴ Background——Due to its strong carbon sequestration ability, the forest ecosystem plays an important role in mitigating the increasingly severe global climate change. Practicing the “Two Mountains” theory, focusing on the goal of carbon neutrality and carbon peak, improving forest carbon sinks is an important path for green development. Therefore, calculating the amount of forest carbon sequestration in Hainan Province, clarifying the level of forest carbon sinks in Hainan Province, and analyzing the forest carbon sinks capacity in Hainan Province in the future can provide a theoretical basis for improving the level of green development in Hainan Province. ⑵ Methods——Based on the forestry data from 2002 to 2022, the forest carbon sequestration in Hainan Province in the past 20 years was calculated by the stock volume expansion method. Then, an index system of three influencing factors including natural factors, economic factors and management factors was established, and the VAR model was used to analyze how these indexes affected the forest carbon sequestration capacity in Hainan Province. Finally, the grey prediction model was used to analyze the changes of carbon sequestration level in Hainan Province in the future. ⑶ Results——First, the level of forest carbon sequestration in Hainan Province showed an increasing trend on the whole, in which the carbon sequestration in understory plants was the lowest, and the carbon sequestration in forestland was the highest. The average annual growth rate of carbon sequestration in the past 20 years was 5. 4%, and the average growth rate reached 7. 8% from 2010 to 2015. This was mainly due to the continuous introduction of forest protection policies in Hainan province. Second, forestry economic factors and management factors had a more obvious long-term impact on the changes of carbon sequestration level. Overall, human activities are gradually having a greater impact on the changes in forest carbon sequestration. The proportion of forest carbon sequestration increased on the basis of the original forests decreased from 100% to 74. 582%, and economic factors and management factors would gradually play a more obvious role in the long run, among which the factor of the total output value of forestry reached 10. 474%, and the factor of forestry practitioners reached 8. 266%, while the impact of natural factors was relatively small. Third, forest carbon sequestration in Hainan Province will increase steadily in the future, but the growth rate will slow down, with an average growth rate of 3. 02%. This is because the current forest coverage rate and forest quality in Hainan Province are relatively high, and the land area of Hainan Island is small, so it is difficult to increase the carbon storage level by expanding the forest area only. ⑷ Conclusions and Discussions——First, improve the forestry circular economy development mechanism. With the development of forestry economy, it is necessary to develop forestry resources within the scope of forest ecological environment in Hainan Province. At the same time, it is necessary to develop and extend the processing chain of forestry industry and forestry-related industries to promote the development of forestry economy. Second, strengthen the forests scientific cultivation, improve the forests management ability and reduce the damage of the original forests environment. Only by strengthening the whole cycle management of forests and adhering to the concept of moderate forests management and scientific forests cultivation can the level of forest carbon sequestration be effectively and stably improved. Third, improve the level of forestry information management. Construct a modern information platform for forestry management, monitor and evaluate forest data accurately and intelligently, and improve the informatization level of forestry managers. [ABSTRACT FROM AUTHOR]

This article examines the challenges that Algerian economic policy has faced in a globalized world from 2000 to 2022 using a VAR model to identify causal relationships between different variables. The results reveal that the Economic Competitiveness Index, Foreign Direct Investments, the exchange rate, money supply, and public expenditures had a positive and significant impact on economic growth in Algeria, while public debt had a negative effect. These findings underscore the crucial importance of balanced management of various economic policy variables to achieve sustainable economic growth in Algeria. They also emphasize the need for policymakers to consider these relationships both in the short and long term when designing and implementing economic policies aimed at promoting the country's economic development. [ABSTRACT FROM AUTHOR]

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Non-state investment capital plays a crucial role in promoting economic development for countries. However, the effectiveness of this investment source is not always as expected. This study aims to evaluate the impact of non-state investment capital on the economic development of the northern province in Vietnam (Thai Nguyen province). Research based on the theory of investment relationship and economic development has been proposed by many researchers. Time series data from 2000 to 2021 are utilized for this study and ARDL approach is employed. The results show that non-state investment capital has a positive impact on economic development. Additionally, the study uses a VAR model to forecast non-state investment capital until 2030. The findings from this research can serve as a reference for policymakers when making decisions to attract investment. The study makes a valuable contribution in forecasting economic growth based on non-state investment capital. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to establish the determinants of production in the Spanish Designation of Origin (DO) area for Cava wine and forecasts sales to establish vineyard area variations that maintain market equilibrium. Design/methodology/approach: By applying a vector autoregressive (VAR) model, the authors forecast demand and the consequent requirements for base wine production. Findings: The results show that Cava sales determine the base wine supply. After forecasting demand and the consequent requirements for base wine, the authors' results show that, to avoid oversupply, the vineyard area for Cava wine should not be increased. Practical implications: The paper develops a simple and effective method for DOs affected by the current European wine plantation regulations to forecast from a supply and demand perspective and their surface needs in response to market changes. Originality/value: This study contributes to the literature because, to the best of the authors' knowledge, no other study has investigated the determinants of Cava supply and demand or defines a model to assess the effects of changes in growing areas. The model is applicable to other European protected designations of origin wines and would help policymakers to accurately establish vine planting authorizations. [ABSTRACT FROM AUTHOR]

Purpose: The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises. Design/methodology/approach: The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables. Findings: This study's results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations. Originality/value: Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy. Highlights: We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR). FCI could become a new target for monetary policy within a hybrid inflation-targeting framework. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment. [ABSTRACT FROM AUTHOR]

In this study, the effect of real effective exchange rate on the data of manufacturing industry export and import, which is obtained by classifying the products according to technology density, is being investigated. It is aimed at determining the effect of real effective exchange rate on import and export of high, medium-high, medium-low and low-technology product groups in the current study, within which the work of quarterly data for the period 1996-2018, for Turkey is taken into account. In accordance with this purpose, in order to determine the relationship between the related variables, Johansen cointegration within the scope of VAR analysis, Toda-Yamamoto causality test, effect response function and variance decomposition methods were used. According to the findings of the analysis when real effective rates and shocks compared in terms of exports high-tech products are more sensitive when compared to other technology products, in terms of imports high technology products are more sensitive to low technology products. Moreover, except for the medium-low technology level, it is observed that the exchange rate elasticity of imports increases as the technology level of the products increases. Therefore, it can be said that the effect of real effective exchange rate on manufacturing foreign trade varies according to technology density for Turkey. According to the Toda-Yamamoto causality analysis, no causal link between the real effective exchange rate and export was found in any of the export models, while the causality relationship between the exchange rate and the import in import models was determined at all technology levels except medium-high technology. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify potential causal relationships between macroeconomic variables and the stock market in Spain. Numerous articles recognize the influence of macroeconomic variables on the stock market and value this knowledge as essential for good investment management. However, there are very few empirical studies that justify the influence of disaggregated macroeconomic variables on the stock market in Spain and vice versa. This article uses the general index of the Madrid Stock Exchange as a proxy variable of the stock market and numerous macroeconomic variables, analyzing monthly data from January 2001 to December 2020 from various published data sources. A descriptive analysis is carried out and a vector autoregressive model (VAR) is applied. Finally, the causality is analyzed identifying the transmission of effects between them. The results confirm the impact of lagged interest rate, monetary aggregate M1 and unemployment rate on the stock market but also identify new features, such as the influence of the stock market on the interest rate, industrial production index, manufacturing activity index and economic sentiment index. This research is useful for Public Administration to detect possible risks in the economy, and it enables investors to better manage their investments. [ABSTRACT FROM AUTHOR]