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The aim of this chapter is to develop a strong research base for the academia and the industry to understand the importance of data analytics in International Trade. This chapter focuses on the case of cotton trade from India and explores different methodologies developed by the World Bank and International Trade Center to analyze the Big Data available on export and import. Through Big Data analysis, this chapter attempts to find out the export performance, market demand, export potential, and attractive markets for Indian cotton. This chapter also explores the trade competitiveness of Indian cotton over the years. The data through appropriate analysis can address some simple yet complicated questions in trade like what export potential the commodity holds, if the commodity is competitive or not in international market, what are new markets to look up to, and other similar questions. In other words, this information could make huge difference in decision-making of traders and policymakers directly, and farmers indirectly.

The economic linkages between China and Japan have been strengthened through both trade and Japanese direct investment in China for past decades. The purpose of this paper is to investigate the impacts of Japanese direct investment in China on the Sino‐Japanese bilateral trade.

An index, RRCA, was used to illustrate the changes of relative comparative advantage of major products, and AR(p) models used to examine the effects of Japanese FDI on both Chinese exports and Chinese imports.

This paper shows that Japanese direct investment in China has contributed not only to the increase of Chinese exports to Japan, but also to the increase of Chinese imports from Japan. This suggests that that the relations between Japanese direct investment in China and the bilateral trade are complementary.

The sample size used in the empirical studies in this paper is very small. In addition, the studies focus only on the effects of FDI on trade while the effects of trade on FDI are neglected.

This paper provides evidence that Japanese direct investment in China is an important determinant factor of the rapid growth of the bilateral trade. It also shows that the appreciation of RMB against Japanese Yen is associated with a decrease in Chinese trade surplus in the bilateral trade.

The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using time-varying copulas.

The time dynamic dependence parameter is allowed to evolve according to a restricted ARMA-type equation which includes a constant term that is driven by a hidden two-state first-order Markov chain.

In using this time dynamics in conjunction with non-elliptical distribution functions and tail dependence measure, the authors are allowing for (and focusing on) non-linearities in the returns-volume-volatility relationship. The results support the assumption that current trading volume provides information about future volatility as well as that there is a negative relationship between returns and their volatilities in financial market indexes.

The authors provide an interesting empirical interpretation for the regimes the authors have identified: in the high dependence regime the sequential information arrival hypothesis and/or noise trading hypothesis are valid, consequently future volatility prediction is possible and persistent but does not last indefinitely; in the low dependence regime, the future volatility prediction is more unlikely to occur, since both trading volume and return negatives have a low (near zero) relation with future volatility.

This study is aimed at better understanding the evolution of inequality in carbon emission in intraincome and interincome groups in the world, and then to uncover the driving factors that affect inequality in carbon emission.

The approach is developed by combining the Theil index and the decomposition technique. Specifically, the Theil index is used to measure the inequality in carbon emissions from the perspective of global and each income group level. The extended logarithmic mean Divisia index was developed to explore the driving factors.

This study finds that the inequality in carbon emissions of intraincome group is getting better, whereas the inequality in carbon emission of interincome group is getting worse. And the difference in global carbon emissions between income groups is the main source of global carbon emission inequality, which is greater than that within each income group. In addition, the high-income group has transferred their carbon emissions to upper-middle income group by importing high-carbon-intensive products to meet the domestic demand, while lower-middle-income group do not fully participate in the international trade.

To alleviate the global carbon inequality, more attention should be paid to the inequality in carbon emission of interincome group, especially the trade between high-income group and upper-middle income group. From the perspective of driving factors, the impact of import and export trade dependence on the per capita carbon emissions of different income groups can almost offset each other, so the trade surplus effect should be the focus of each group.

In order to consider the impact of international trade, this study conducts a comprehensive analysis of global carbon emissions inequality from the perspective of income levels and introduces the import and export dependence effect and the trade surplus effect into the analysis framework of global carbon emission inequality drivers, which has not been any research carried out so far. The results of this paper not only provide policy recommendations for mitigating global carbon emissions but also provide a new research perspective for subsequent inequality research.

This paper aims to capture tail dependence between sentiment index and Shanghai composite index (SCI) by proposing a sentiment index based on text mining.

Online text mining and the Copula model were used in this study.

First, the paper finds herding effect in the expression of investors’ sentiment from online text data, and the usage occurrence frequency of most vocabulary is less correlative with SCI. Second, given these two features, the paper uses weighted divide-and-conquer algorithm to construct a sentiment index. Finally, because of multivariate non-Gaussian joint distribution between them, the paper uses the Copula model to detect their tail dependences, and finds that both upper and lower tail dependences could have a significant influence between positive sentiment and SCI, with a higher probability on the upper one. Additionally, only the upper tail dependence exhibits the significant influence between negative sentiment and SCI.

This paper proposes a framework of constructing investment sentiment index with the weighted conquer-and-divide algorithm, and characterizes tail dependence between sentiment index and SCI. The implication can measure the environment of investment market of China and provide an empirical ground for bandwagon effect and bargain shopper effect.

The purpose of this paper is to determine how an index of agribusiness stocks performs relative to the S&P 500 particularly in times of recession.

Using value-weighted indexes of agribusiness stocks, large cap US stocks, and copula estimation, the paper quantifies the correlation in potential investment portfolios. The information obtained from the copula estimated dependence measures and Value at Risk (VaR) allows to examine the diversification benefits of holding agribusiness stocks in the portfolio relative to the S&P 500.

The results provide limited evidence that the addition of agribusiness stocks to a portfolio are able to provide significant diversification benefits to a portfolio of domestic equities, as represented by the S&P 500 index. The VaR analysis also indicates the risk of extreme losses remained relatively stable both across time and portfolio weightings.

While this research examines a broad-based agribusiness stock index, there exists a number of sub-assets classes within the analyzed index that should be analyzed to see if the offer benefits to investors. In addition, only stocks traded on US-based stock indexes are included in this analysis; as such, the authors would like to extend the research to have a more global approach.

The findings suggest that investors who are looking to a broad-based agribusiness stock index to provide more diversification in their portfolio, may find it unattractive from a both a risk management and profit maximizing perspective. However, that does not mean that the agribusiness stock index might be an affective complement to a portfolio that contains multiple other assets classes.

The issue of correlation convergence during financial crises is one of great concern to investors. To the authors’ knowledge, this is the first paper that uses copulas to evaluate the role of agribusiness stocks in an investor's portfolio.

The issue of export instability exerts an enduring fascination for economists with an interest in the area of economic development. Over several decades a voluminous literature has emerged embracing debates on the domestic consequences and on the causes of export instability. The purpose here is to examine these debates and an attempt is made to set out different theoretical stances, to classify and examine empirical findings, and to indicate the directions in which the debates have moved. Such a statement of a review article's purpose is, of course, incomplete without more specific delineation of the boundaries within which the general objectives are pursued. Here that delineation has three facets.

The objective of this paper is two-fold. First, through the use of two indices that correct for the bias introduced by the relative size of the U.S. economy, it describes the degree of regional dependence reached in the NAFTA area as compared to Europe. Second, it asks whether the stage is set for the successful adoption of a single currency, namely the dollar, within the area. In an attempt to answer this question, the paper identifies the one critical asymmetry that should be addressed before considering the complete monetary integration of the area.

This paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020.

The authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test.

(a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers.

For the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets.