Analyzing the impact of remittance, FDI and inflation rate on GDP: A comparative study of Bangladesh, Pakistan and Sri-Lanka using VAR and BEKK-GARCH approach.

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

The time-varying dynamic impact of US renewable energy prices on agricultural prices in China: the case of fuel ethanol.

This paper intends to look into the time-varying dynamic impact of US fuel ethanol, one of the renewable energy sources, on the prices of agricultural products (specifically corn, soybeans, rice, and wheat) in China based on monthly price data from January 2000 to January 2023. To achieve this, a time-varying parameter vector autoregressive (TVP-VAR) model is employed, which takes into account structural changes in emergencies through time-varying parameters. The empirical results show that the equal-interval impulse responses of price fluctuations in agricultural commodities are primarily positive to variations in fuel ethanol prices and production. And the intensity and direction of the effects vary at distinct time lags. Additionally, the magnitude of these responses is most pronounced in the short term for all agricultural commodities except for corn, and the duration of the impulse responses at different time points is generally longer for corn prices compared to other commodities. The study also reveals that the influence of US fuel ethanol on Chinese agricultural commodity prices is not substantial on the whole. Therefore, there is a necessity to advance the growth of biofuels and provide policy support and financial subsidies for agricultural products earmarked for food production. These actions could shed insights into the progression of Chinese renewable energy and food policies, ensuring the stability of the market in the long run.

Economies of scale in Saudi Arabia's refining sector: An application of modern econometric models.

This paper aims to reveal how the refining industry's inputs in Saudi Arabia affect its output and to forecast refining industry dynamics. The variables used in this paper are the refined petroleum products representing the dependent variable, with natural gas liquids, crude oil, labor, and capital acting as explanatory variables covering the period 1990-2020. The long run cointegration of the variables was observed. An error correction model utilizing the Cobb-Douglas production function framework was performed. Furthermore, this study applied the vector autoregressive model (VAR) and its diagnosis tests, including forecast-error variance decomposition (FEVD) and impulse response functions (IRFs). The results indicate that natural gas liquids and crude oil have a significant influence on the refining industry's output. Although capital and labor are significant determinants of output, they do not contribute significantly to output creation in the refining industry. This might be related to some parts of the capital and human resources being directed toward supporting activities, such as administration, technical support, maintenance, transportation, logistics and assigning third-party contractors to perform the main duties related to the production process. Additionally, the petroleum refining industry requires substantial capital resources for construction and maintenance. Thus, the actual measurement of capital input's influence on output was observed in the long run. The results reveal that the refining industry's variation is influenced by both its own characteristics and that natural gas liquid, crude oil, capital, and labor factors have a significant impact on the accuracy of industry forecasts. This study concludes that Saudi Arabia's petroleum refining industry operates under decreasing returns to scale, while the shocks in the refining industry are influenced and caused by external factors.

Unlocking Urbanization: The symbiotic relationship between inclusive finance and urban development in China.

The emergence and development of inclusive finance has made it possible for more economic entities to have easy access to a wider selection of financial services. This shift has significantly addressed the financial challenges inherent in the process of urbanization, making it a driver of the process of urban development. Therefore, this paper provides empirical evidence on the relationship between financial inclusion development and urbanization construction in China using provincial data and a panel-VAR model. The results show that: (1) There is a significant co-integration relationship among inclusive finance, urbanization, government support, and real estate development. (2) Inclusive finance has a long-term positive impact effect on urbanization. (3) Population urbanization has a positive impact on inclusive finance, but income urbanization has a negative impact on inclusive finance. To effectively promote the development of inclusive finance and urbanization, the following measures are of utmost importance: Firstly, while accelerating urbanization construction, it is necessary to expand and enhance the coverage of financial services. This will ensure that multiple regions can benefit from financial services. Secondly, to meet the diverse needs of different regions, more targeted financial products should be developed, making full use of the advantages of inclusive finance. Lastly, the government should strengthen its supervision of financial institutions and reduce the risks associated with inclusive finance, thereby ensuring a positive interaction between inclusive finance and urbanization development.

Impact of capital market volatility on economic growth - An analysis based on stochastic volatility model.

This paper delves into the relationship between the volatility of the capital market and economic growth within the broader framework of the macro capital market. By employing the Heston stochastic volatility model in tandem with macroeconomic theory, we aim to analyze the stochastic control problem between the allocation trajectory of macro-capital and economic fluctuations. Our mathematical analysis reveals that the influence of capital shifts on economic growth's volatility varies across different capital markets due to diverse risk levels inherent within the macro-capital market. To validate these mathematical findings, we embark on an empirical econometric analysis tailored to the nuances of China's capital market and its macroeconomic operations. This econometric exploration yields two primary insights: 1. Distinct components of China's capital market have varying influences on macroeconomic growth. 2. The structure of China's capital market, especially in its impact on macroeconomic development, exhibits imbalances and lacks optimal configuration.

Navigating the nexus: Geopolitical risk, fossil energy prices, and European utility stock returns - Implications for environmental management and energy security in a conflict-ridden global landscape.

This study employs a TPV-VAR analysis method to explore the linkage between GPR, fossil energy prices, and utility stock returns across 16 European countries from August 2009 to April 2023. Our findings reveal variations over time in how GPR influences the prices of fossil energy and utility stock returns. GPR significantly influences stock returns in the short term (1 month), with prolonged effects observed during major geopolitical incidents, while showing no significance in the medium (6 months) and long term (12 months). Further, the Russia-Ukraine War had a more pronounced impact on fossil energy prices and utility stock returns compared with the Arab Spring and Brexit. Finally, GPR shocks exhibit heterogeneous effects on different fossil energy types, with oil prices being more affected than coal and gas prices. Energy prices act as a channel through which GPR influences utility stock returns. This study elucidates the linkage between GPR, prices of fossil energy, and stock returns, offering valuable perspectives for governments and investment decision-makers into risk management.

Assessing the impact of water-sediment factors on water quality to guide river-connected lake water environment improvement.

The substantial impacts of exogenous pollutants on lake water quality have been extensively reported. Water-sediment factors, which are essential for regulating water quality in river-connected lakes, have not been studied in depth under different hydrological conditions. This study has combined a 31-year water environmental dataset (1991-2021) regarding Dongting Lake and a vector autoregression model (VAR) in order to investigate the impulse response characteristics and contributions of water quality caused by water-sediment factors across different periods. Our analysis suggests that total nitrogen (TN) exhibited a significant increasing trend, whereas total phosphorus (TP) increased to 0.17 mg/L, and then decreased to 0.07 mg/L from 1991 to 2021. The inflow of suspended sediment discharge (SSD) decreased significantly during the study period, mainly because of the decrease in SSD in the three channels (TC). In the pre-Three Gorges Dam (TGD) period, water discharge (WD) and SSD were the Granger causes of TN and TP. In the post-TGD periods this relationship disappeared because of the construction of the TGD, which reduced the inflow of SSD and WD into the lake. Water quality indicators showed an instant response to the shock from themselves with high values, whereas the impulse response of the water quality to water-sediment factors exhibited lagged variations. This meant that the water quality indicators displayed a high impact by themselves across the different periods, with values varying from 67 % to 95 %. Water level (WL) and SSD were the predominant water-sediment factors for TP in the pre-TGD period, with the impact on TP changes accounting for 11 % and 9 %, respectively, whereas the contribution of SSD decreased to 2 % in the post-TGD period. WL was the most crucial water-sediment factor for CODMn during the different periods, with contributions varying from 17 % to 20 %. To improve the water quality of Dongting Lake, in addition to the implementation of strict controls on excessive external nutrient loading, regulating water-sediment factors according to the hydrological features of Dongting Lake during different periods is vital.

Time-varying impact of income and fossil fuel consumption on CO2 emissions in India.

This paper investigates the time-varying effects of fossil fuel consumption on CO2 emissions in India utilizing the time-varying cointegration test, allowing for multivariate long-run time-varying cointegration parameter developed by Bierens and Martins (2010) and the time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005). The long-run time-varying coefficients reveal that GDP has a positive and increasing impact on CO2 emissions over time. Moreover, results confirm the polluting effects of all fossil fuels. Besides, the TVP-VAR model findings also demonstrate that changes in income and fossil fuel consumption have a positive and significant impact on environmental degradation. Coal is found to be the most polluting fuel, followed by oil consumption. Furthermore, the time-varying responses show that increased natural gas consumption has the least influence when compared to other fossil fuels on CO2 emissions.

The complexity of financial development and economic growth nexus in Syria: A nonlinear modelling approach with artificial neural networks and NARDL model.

This study investigates the complex interaction between financial development (FD) and economic growth (EG) in Syria from 1980 to 2018 using advanced nonlinear modeling techniques including artificial neural network VAR models, nonlinear causality tests, and nonlinear autoregressive distributed lag (NARDL) models. The results indicate that linear models are inadequate to capture the data patterns, necessitating nonlinear approaches. The artificial neural network VAR model reveals a nonlinear connection between FD and EG. The nonlinear causality test confirms that FD causes EG in a nonlinear manner. The NARDL (1, 1, 0, 1, 1) model is selected based on Akaike information criterion and diagnostics. The findings show a long-run equilibrium and short-run dynamics between FD and EG in Syria. Moreover, positive changes in FD have stronger, more persistent effects on EG compared to negative changes, implying asymmetry. Additionally, the impact of FD on EG is nonlinear, varying with FD levels. These results support recent studies suggesting a nonlinear nexus between FD and EG. They also lend support to the finance-led growth theory while opposing the "too much finance harms growth" hypothesis. The study offers policy implications for Syria to create conditions conducive to positive FD shocks and adopt a long-term perspective regarding FD-EG policies.

Towards a dynamical understanding of microstate analysis of M/EEG data.

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak.

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions. The study further utilises the entropy transfer approach to capture the Granger-causal relationship in the presence of nonlinearities of the data series. The results indicate a high information flow from fuel prices to the FTSE-100, Pacific, and European stock indicies, but not the other way round. The results show that, for the FTSE-100 and the European region, there is a two-way information flow between equities and natural gas, and vice-versa. However, a one-way information flow was established from the stock market to the Pacific and emerging economies.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Decoupling between Economic Growth and Carbon Emissions: Based on Four Major Regions in China.

This paper constructs a decoupling model for four major economic regions of China, based on the Tapio decoupling index method and VAR model for carbon emissions to compare and measure the impact of decoupling between carbon emissions and economic growth in China during 1997 to 2019. The results show that the degree of decoupling between economic growth and carbon emissions varies among different economic regions, and the decoupling status is better in all regions at the beginning of the 21st century. In general, the decoupling status in the eastern and western regions is more ideal than that in the central and northeastern regions. The impulse response and variance decomposition results show that renewable energy consumption could always reduce the growth of carbon emission intensity, and its effects are most significant in the western region. The above findings help to reveal the link between economic growth, renewable energy consumption and carbon emissions in China in recent years, and how to ensure a stable economic growth in China while increasing the share of clean energy consumption in each region to achieve carbon neutrality.

Gold or Bitcoin, which is the safe haven during the COVID-19 pandemic?

This study compares the dynamic spillover effects of gold and Bitcoin prices on the oil and stock market during the COVID-19 pandemic via time-varying parameter vector autoregression. Both time-varying and time-point results indicate that gold is a safe haven for oil and stock markets during the COVID-19 pandemic. However, unlike gold, Bitcoin's response is the opposite, rejecting the safe haven property. Further analysis shows that the safe-haven effects of gold on the stock market become stronger when the pandemic critically spreads.

A Study on the Transnational Spillover Effects of Bank Risk and Sovereign Risk-From the Perspective of COVID-19 Epidemic Situation.

In recent years, the world economy and the global financial system have closely intertwined, deepened economic and financial integration via cross-border investments, financings, imports, and exports. Since banks serve as the core of a country's financial system, the risk status of banks directly affects the country's national credit and financial security. The current complexities of the international and domestic environments are increasing geopolitical risks. Moreover, there is increasing uncertainty recognition in the financial and economic development of all countries, more systemic banking risks, and sovereign risk transfer elements. In this scenario, resisting external risk input is essential to enhance risk prevention ability. Therefore, this paper adopted the VAR-based time domain and frequency model for a multi-dimensional analysis of the two perspectives of banking and sovereign risk spillover effects. The empirical results indicate that the entire sample under the static overflow effect always shows that most of the absorption is the banking sector risk, and sovereign risk is the leading risk spillover. In the frequency domain perspective, the short-term spillover effects between bank and sovereign risk are dominant. Moreover, in relation to the outbreak and continuous spread of the COVID-19 pandemic, the spillover effects are often dominated by adverse, long-term scenarios.

Impact of the COVID-19 pandemic on the housing market at the epicenter of the outbreak in China.

The outbreak of the COVID-19 in January 2020 has had a profound impact on the global economy, so it is important to study the impact of the pandemic on the housing market. To investigate the impact of the pandemic on the housing market and the response of the housing market, this paper first uses the hedonic price model to compile the second-hand housing price index in Wuhan and its neighboring capital cities and then uses the difference-in-difference (DID) model to conduct a comprehensive study on new commercial housing and second-hand housing market. In addition, this paper also uses the VAR model to explore the housing market's response to the epidemic situation. The results show that the negative impact of the pandemic on the housing market is mainly reflected in the volume and area of housing transactions, with little impact on housing prices. Second, the reported cases of COVID-19 have a negative impact on the housing market in the short term, which gradually weakens with time and disappears after three weeks. This paper's findings indicate that the epidemic's impact on the housing market is mainly due to the real estate enterprises stopping selling houses and local governments implementing home quarantine measures, which affect normal housing transactions. However, the COVID-19 pandemic did not greatly negatively impact consumers' demand and confidence in buying houses, so the house prices remained stable overall. Supplementary Information: The online version contains supplementary material available at 10.1007/s43546-022-00225-2.

Determining the COVID-19 effects on spillover between oil market and stock exchange: a global perspective analysis.

This paper investigates volatility spillovers between the global crude oil market and the stock markets of the global oil stock markets (Russian, Canada, China, Kuwait, and the USA) pre and after the COVID-19 pandemic. We use wavelet Granger causality methods to study the volatility spillovers between global oil stock markets, mainly from January 1, 2019, to March 31, 2021. Our Results (1) shows that WTI and Brent oil prices had a negative mean return before COVID-19 but a positive mean return during the pandemic spread. Other Results (2) find the positive, significantly lowest, and highest frequency during the COVID-19 outbreak for all selected countries. The results also show that the link between oil WTI & Brent prices and stock markets return in the lowest (33-66 days) and highest frequency range (4-16) before the Covid-19 epidemic, especially in the first quarter of 2020. Before the COVID-19 period, the Russian oil stock market is seriously prejudiced with oil prices on a modest scale, but not after the pandemic's start. This study also perceives direction opposite between the COVID-19 period. The Canadian and United States America oil and stock markets influence the lowest scale in the previous COVID-19 sample for the U.S. market. Moreover, this paper exposed that oil marketing highest oil futures in their portfolios than stock shares for all times. We found that oil price shocks had a more significant impact on the stock markets of the United States and Canada than on the stock markets of other countries.

Time-varying spillovers among pilot carbon emission trading markets in China.

Clarifying the time-varying spillovers among pilot carbon emission permit trading markets in China is an important foundation for building the national carbon emission trading market. We calculate the dynamic spillover of carbon price return among the pilot carbon emission permit trading markets in China with the time-varying connectedness approach. The dataset is constructed from transaction data from seven pilot carbon markets in China during the period of June 23, 2014, to December 31, 2020. The quantitative analysis suggests that (i) Beijing and Chongqing carbon emission trading markets are the main spillover markets of carbon price returns, with strong pricing power, while the Guangdong and Tianjin markets are the main receivers of the price return spillover in other pilot carbon emission trading markets. (ii) The spillover effect among China's carbon markets has a strong policy orientation. The improvement and development of the carbon market driven by macroeconomic regulation and control policies can effectively improve the spillover ability of the carbon market, and the market trading activity, namely the volatility of the carbon price return rate, can amplify the spillover ability of the carbon market in the short term. (iii) There exist three types of price return spillover among China's pilot carbon emission trading markets, including central divergence, one-way chain transmission, and circular spillover. Along with the improvement of market operation efficiency, the central divergent type of spillover shifts to the pattern of circular spillover. It is necessary for the government to improve market efficiency and ensure the coordinated development of China's pilot carbon emission trading market and national carbon emission trading market.

Spillover effect of greenhouse gas emissions across five major continents.

There are some studies that either examine the convergence or spillover effect of greenhouse gas emissions (GHGEs) at the country or multiple country level but research on the spillover effect of GHGEs is very limited particularly for the agriculture sector across the major continents. Therefore, this study examined the connectedness across the five major continents namely Asia, Africa, Europe, Oceania and Americas for the period 1961-2018. To achieve the objective, this study applies a very recent time-varying parameter vector autoregressive (TVP-VAR) model. The results obtained from TVP-VAR model indicate that all the five continents are highly interconnected in GHGEs. In particular, the findings show that Asia, Africa and Americas are the main transmitters and Europe and Oceania are the main receivers of GHGEs. The findings suggest that Asia, Africa and Americas should use the economic profits received from the economic integration for the environmental protection policies with more attention. This might help for high economic growth and development with a clean environment.

Economic Policy Uncertainty (EPU) and China's Export Fluctuation in the Post-pandemic Era: An Empirical Analysis based on the TVP-SV-VAR Model.

In the COVID-19 pandemic, the bidirectional policy adopted by the governments to stimulate domestic economy and reinforce foreign trade control is making the trade environment abnormally complex. China is facing a new challenge in export trade growth. Based on the continuous monthly data from January 2002 to April 2021, this paper uses the time-varying TVP-SV-VAR model to study the impulse response of China's export trade to economic policy uncertainty (EPU). It is found that (1) on the whole, the shock of global EPU and China's EPU on China's export to the OBOR/RCEP member countries is time-varying, different, and structurally significant; (2) during the pandemic, EPU has a significant short-term negative shock on China's gross exports and export to OBOR/RCEP members, and this shock is especially big in the case of global EPU. In the post-pandemic era, China should strengthen pandemic control and economic risk monitoring, continue with execution of multilateral FTAs and create a sustainably stable export trade environment.