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This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

The purpose of this paper is to construct a frequency-domain framework to study the asymmetric spillover effects of international economic policy uncertainty on China’s stock market industry indexes.

This paper follows the time domain spillover model, asymmetric spillover model and frequency domain spillover model, which not only studies the degree of spillover in time domain but also studies the persistence of spillover effect in frequency domain.

It is found that China’s economic policy uncertainty plays a dominant role in the spillover effect on the stock market, while the global and US economic policy uncertainty is relatively weak. By decomposing realized volatility into quantified asymmetric risks of “good” volatility and “bad” volatility, it is concluded that economic policy uncertainty has a greater impact on stock downside risk than upside risk. For different time periods, the sensitivity of long-term and short-term spillover economic policy impact is different. Among them, asymmetric high-frequency spillover in the stock market is more easily observed, which provides certain reference significance for the stability of the financial market.

The originality aims at extending the traditional research paradigm of “time domain” to the research perspective of “frequency domain.” This study uses the more advanced models to analyze various factors from the static and dynamic levels, with a view to obtain reliable and robust research conclusions.

This paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns.

This paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility.

The results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects.

The presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.

The authors test the granularity hypothesis to international inflation spillovers using annual exports and inflation data for 138 countries from 1991 to 2020. This study aims to discuss the aforementioned objective.

First, the authors quantify the power law for the right tail of the export volumes distribution and discuss its implications. Then, the authors compute the granular residual, a measure of shocks to the largest countries.

The authors find export volumes across countries are not Gaussian-distributed but follow a power law. This finding means the largest countries disproportionately impact world inflation. In addition, the authors find that countries with higher relative weight in international trade determine a portion of international spillovers greater than their trade share. Moreover, eight big grains are responsible for the bulk of inflation spillovers.

The policy implication is that other countries' central banks should closely monitor the eight big grains when conducting their domestic monetary policy.

This is the first study spotting the problem of granular inflation spillovers.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

This study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

With network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

In seminal work, Den Haan et al. (2007, 2010, 2011) show business loans respond in the opposite direction of what may be intended by monetary policy action in the United States and Canada. Based on various approaches, identification schemes, and samples, we document evidence this loan puzzle is not exclusive to developed economies but is also pervasive in emerging markets. We find business loans generally decline following expansionary monetary policy shocks. A preponderance of statistical and structural evidence indicates important transmissions of this puzzle from the United States to emerging markets.

The purpose of this paper is to explore automobile fuel efficiency policies in the presence of two externalities: a global environmental problem and international innovation spillovers.

Using a simple model with two regions, the authors show that both a fuel tax and a tax on vehicles based on their fuel economy rating are needed to decentralize the first best.

If standards are used instead of taxes, the authors find that spillovers may alleviate free-riding. Under some conditions, a strict standard in one region may favor the adoption of a strict standard in the other one.

The authors also show that if policies are not coordinated between regions, the resulting gas taxes will be set too low and each region will use the tax on fuel rating to reduce the damage caused by foreign drivers.

The global financial crisis has magnified the role of Financial Sector Surveillance (FSS) in the International Monetary Fund's activities. This chapter surveys the various steps and initiatives through which the Fund has increasingly deepened its involvement in FSS. Overall, this process can be characterised by a preliminary stage and two main phases. The preliminary stage dates back to the 1980s and early 1990s, and was mainly related to the Fund's research and technical assistance activities within the process of monetary and financial deregulation embraced by several member countries. The first ‘official’ phase of the Fund's involvement in FSS started in the aftermath of the Mexican crisis, and relates to the international call to include financial sector issues among the core areas of Fund surveillance. The second phase focuses on the objectives of bringing the coverage of financial sector issues ‘up-to-par’ with the coverage of other traditional core areas of surveillance, and of integrating financial analysis into the Fund's analytical macroeconomic framework. By urging the Fund to give greater attention to its member countries' financial systems, the international community's response to the global crisis may mark the beginning of a new phase of FSS. The Fund's financial sector surveillance, particularly on advanced economies, is of paramount importance for emerging market and developing countries, as they are vulnerable to spillover effects from crises originated in advanced economies. Emerging market and developing economies, which constitute the majority of the Fund's 187 members, are currently the recipients of over 50 programmes of financial support from the Fund (including those of a precautionary nature), totalling over $250 billion.

The purpose of this paper is to look at the factors driving labour creation in Russia, while paying attention to the role of innovation policy. The study considers innovation variables with indicators linked to social conditions (social filter component) and geographical spillovers for 85 regions during the period 2010-2016.

In particular, the study uses latitude and longitude coordinates to compute the distance between Russian regions according to the Haversine formula. In this manner, it measures the spillovers as the weighted sum of R&D capital stock on the basis of computed distance, according to the accessibility index procedure.

The finding is very important in terms of policy implications for supporting employment. As the results stress that own innovation produces labour creation effects, while knowledge spillovers are labour-saving, the study could conclude that regional innovation policy may have undetermined the objective of an efficient level of absorptive capacity able to benefit positively from external innovation.

The study contributes to the literature by exploring whether geographical spillovers are labour-friendly or labour-saving in Russia.