Intensity Of Spillovers Research Articles

The Importance of Non-Systemically Important Banks—A Network-Based Analysis for China’s Banking System

There is important theoretical and practical significance to scientifically identifying the systemic importance of banks for effectively preventing and controlling systemic risks in the banking system. Prevalent identification methods are biased because they only pay attention to measuring the systemic risk contribution of individual banks to the whole system in order to determine that bank’s systemic importance. Less attention is paid to the cascade effects of risk spillover among banks. This study proposes a novel method for measuring the cascade effects of risk spillover of banks and their contributions to systemic risks by building up a conditional tail risk network of China’s banking system. Different from previous analyses of systemic risks based on the identification and risk measurement of systemically important banks (SIBs), this paper focuses on analyzing the risk spillover effects of non-SIBs and their contributions to systemic risks by building up a conditional tail risk network of China’s banking system. Our empirical results show that some non-SIBs in China are more vulnerable to the shocks of systemic risk than SIBs, and that they are more likely to act as key intermediaries to transmit risk to SIBs, thereby triggering systemic risk. In view of this, we propose to identify key non-SIBs according to their risk spillover intensity because they are also systemically important. The market regulators not only need to pay attention to SIBs that are too big to fail, but also treat seriously the key intermediaries of “risk spillover too strong to fail” in the network in order to ensure the stability of the banking system.

Spatiotemporal Evolution of Urban Resilience and Spatial Spillover Effects in Guangdong Province, China

In the context of global environmental changes, the frequency of various disasters and extreme events is increasing, and enhancing urban resilience has become an important guide for current urban development. Previous studies have mainly focused on changes in urban resilience, with less consideration for the impact of spatial spillover effects on urban resilience. Therefore, this paper aims to analyze the temporal and spatial evolution of urban resilience and its spatial spillover effects in Guangdong from 2012 to 2020 based on the urban resilience assessment model, the Getis-Ord Gi* model, and the improved Capello model. The results are as follows: Affected by COVID-19, the urban resilience of Guangdong Province declined from 2019 to 2020, and 42.86% of the cities demonstrated negative growth in their urban resilience. Urban resilience exhibited significant spatially non-equilibrium characteristics among different cities. The urban resilience of the cities in eastern, northern, and western Guangdong, which are the “collapse zone” of urban resilience, was lower than 0.229 from 2012 to 2020. The intensity of urban resilience spillover in Guangdong presented a typical three-level circle structure of “core-subcore-periphery”, which decreased from the core circle to the surrounding circle. COVID-19 caused a 27.21% decrease in the total urban resilience spillover in Guangdong from 2019 to 2020. Finally, we identified critical driving factors of urban resilience using the optimal-parameters geographical detector model. This study can provide a scientific reference for the Chinese government to build resilient cities and improve sustainable urban development.

A multi-objective optimization algorithm based on the identification of risk spillover intensity measurement in Chinese financial markets

Abstract Identifying the intensity of risk spillover in China’s financial market can provide an important empirical basis and information reference for monitoring and preventing financial risks. This paper uses a multi-objective application of particle swarm optimization algorithm under a multi-objective optimization algorithm to induce the MOPSO algorithm. The algorithm identifies and measures the intensity of risk spillover in China’s financial market in two aspects, including the risk spillover from policy instability to the stock market and the two-way risk spillover between the financial industry and the real estate industry. Regarding policy instability, the risk spillover intensities of downside fiscal policy, monetary policy, trade policy, and foreign exchange policy are 14.83%, 53.88%, 7.54%, and 31.06%, respectively. Regarding the two-way risk spillover intensity, the average risk spillover intensity of real estate to finance is 70.28%, which is 5.57 percentage points higher than that of finance. This indicates that the multi-objective optimization algorithm can identify and measure the risk spillover intensity of China’s financial market, providing data support and information reference for preventing financial risks.

Does free brand spillover benefit online retailers? Roles of logistics service selection

Brand spillover is a pivotal tool that online retailers have adopted to market their new brand products, especially when competing against well-known product brands that prone to free-riding. This common practice seems beneficial to the adopters, but the motivation remains unclear in a supply chain setting. This study explores a co-opetitive supply chain in which a famous brand manufacturer (she) sells her own-branded products by commission via an online retailer (he) with a private label, who determines whether or not to utilize free brand spillover. The upstream manufacturer has to decide between two logistics service strategies, namely, Scenario T (i.e., using third-party logistics to serve the sales of their own-branded products) and Scenario R (i.e., employing the high-quality logistics of the online retailer). We explore the strategic interactions of the two firms and characterize their equilibrium. We reveal that when the decreased brand attractiveness of implementing third-party logistics is low, the brand manufacturer opts for the third-party logistics; otherwise, she embraces the high-quality logistics of the retailer. Interestingly, the brand manufacturer may be less inclined to utilize the logistics service of the retailer if he employs free brand spillover because it will help intensify market competition from the manufacturer’s perspective. Moreover, although free brand spillover enhances the attractiveness of the retailer’s private label without any cost, the retailer may not adopt it when the brand spillover intensity is low and the decreased brand attractiveness of the third-party logistics is moderate. Finally, we provide managerial insights and guidance for the firms’ managers on how to use the above strategies to obtain a “win–win” outcome. We also extend our analysis to the setting with imperfect substitutes, where the deterrence effect of brand spillover on the manufacturer’s logistics choice remains the same.

The risk spillover of high carbon enterprises in China: Evidence from the stock market

This paper examines the time-varying risk spillover among high carbon enterprises by constructing Granger-causality networks from 2011 to 2021, and explore the types of high carbon enterprises that have stronger risk spillover capabilities. Additionally, we also expanded our analysis on the risk spillovers from high carbon enterprises to non-high carbon enterprises. We find that the intensity of risk spillover among high carbon stocks strengthened from 2013 to 2016, then weakened, and slightly re-strengthened in 2020. High carbon enterprises from power, steel, aviation, and petrochemical industries occupy central positions in the networks. Moreover, fundamental factors such as asset scale, profitability, capital structure, ownership, and industry category significantly influence the connectivity and centrality of high carbon enterprises in the networks. Meanwhile, the high carbon enterprises with higher connectivity and central location in the networks have greater systemic risk contributions. Finally, we find that the non-high carbon enterprises from agriculture, forestry, animal husbandry and fishery industry, mining industry and transportation, warehousing and postal industry are more susceptible to the risk overflow from high carbon enterprises.

Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities

The main focus of this research is to investigate the potential spillover effects between AI-based stocks and tokens by using the quantile connectedness approach developed by Ando et al. (2022). The study aims to investigate both static and dynamic spillovers at the lower and upper tails of the return distribution. AI-based stocks and tokens may have relatively low levels of connectedness, which also varies over time and increases during periods of economic turbulence. In addition, in line with previous work analysing other financial markets and assets, this research finds that the system is more sensitive to the tails of the distribution (i.e., the lower and upper quantiles) than to the median (Q = 0.50). This finding is consistent with expectations, and measures of dynamic connectedness change over time, with the intensity of spillovers increasing at the extremes of the distribution. These results have practical implications for portfolio managers, as they can use the results to adjust their investment portfolios according to the evolution of the dynamic spillovers observed in the system. Overall, this study sheds light on the potential tail spillovers in the AI-based stock and token market and provides valuable insights for investment decisions.

A Multi-Regional Input–Output Model to Measure the Spatial Spillover of R&D Capital

Reallocating innovative capital elements can improve the growth of total factor productivity and promote high-quality economic development. The multi-regional multiplier model measures the spatial spillover effects of R&D capital to trace the interregional R&D flows and explore the engines of the longer-term economic growth in China. Results show that the direct R&D intensity in different regions is all concentrated in basic research sectors supported by government funds, and decreased from coastal areas to inland areas. Second, R&D gradually flowed from China’s coastal regions to inland regions, from upstream basic research sectors to downstream infrastructure construction sectors. Third, Guangdong, Jiangsu and Beijing are the main contributors, with R&D spillover intensities reaching 1.69%, 1.40%, and 1.37%, respectively. Xinjiang, Tibet, and Hainan are the main beneficiaries, with R&D inflow intensities reaching 0.49%, 0.53%, and 0.50%, respectively. Finally, the channel of R&D spatial spillover manifests a circular distribution and contact-type and jump-type modes.

Investing in a shared supplier to encourage environmental responsibility under spillovers and demand uncertainty

In this paper, we investigate the issue of whether two competing manufacturers should make environmental quality investments in a shared supplier under spillovers and demand uncertainty. To this end, we develop game-theoretic models and analyze the equilibrium outcomes in cases with no investment, unilateral investment, and bilateral investment. The results show that if the manufacturers are less (more) risk-averse, both (none) of them invest(s). If one manufacturer is less risk-averse and the other is more risk-averse, they act as the unique investor and free-rider, respectively. If both are moderately risk-averse, there are thresholds below which a manufacturer invests; otherwise, they jointly invest or exit. Moreover, any manufacturer may suffer from the competitor’s investment despite the existence of spillovers. In some cases, the bilateral investment is detrimental to the supplier and may yield a win–win, win–lose, lose–win, or lose–lose outcome for the manufacturers. Our sensitivity analysis indicates that, under medium risk aversion, the optimal investment strategies are synchronous if the intensity of spillovers (product substitutability) is high (low); and they are asynchronous otherwise. In general, increased demand uncertainty or customers’ environmental sensitivity strengthens the incentives for the manufacturers to invest in the shared supplier. Our key findings remain valid under asymmetric spillovers, Bertrand competition, investment cost-sharing, etc.

Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic

This paper examines the effects of the COVID-19 outbreak, recent oil price fall, and both global and European financial crises on dependence structure and asymmetric risk spillovers between crude oil and Chinese stock sectors. Using time-varying symmetric and asymmetric copula functions and the conditional Value at Risk measure, we provide evidence of positive tail dependence in most sectors using copula and conditional Value-at-Risk techniques. We can see the average dependence between oil and industries during the oil crisis. Moreover, we find strong evidence of bidirectional risk spillovers for all oil-sector pairs. The intensity of risk spillovers from oil to all stock sectors varies across sectors. The risk spillovers from sectors to oil are substantially larger than those from oil to sectors during COVID-19. Furthermore, the return spillover is time varying and sensitive to external shocks. The spillover strengths are higher during COVID-19 than financial and oil crises. Finally, oil do not exhibit neither hedge nor safe-haven characteristics irrespective of crisis periods.

Risk Measure between Exchange Rate and Oil Price during Crises: Evidence from Oil-Importing and Oil-Exporting Countries

This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to analyze the dynamic conditional correlations. Moreover, the Gumbel copula is employed to define the nonlinear structure of dependence and to evaluate the optimal portfolio. The conditional Value-at-Risk (CoVaR) is adopted as a risk measure. Findings indicate a long-run dependence and asymmetry of bidirectional risk spillover among oil price and exchange rate and confirm that the risk spillover intensity is different between the former and the latter. They show that the oil price has a stronger spillover effect in the case of oil exporting countries and the lowest spillover effect in the case of oil importing countries.

Dynamic connectedness among Pakistani stock markets and its major trading partners

PurposeInternationalization and financial deregulation have caused market participants and policymakers to consider the significance of financial connectedness and the spillover effects of shocks. In this context, this research is a pioneering effort to investigate the direction and magnitude of return volatility spillovers between Pakistan’s financial markets and those of its key trade partners. This paper examines the relationship between return and volatility spillover in the financial markets of Pakistan and its major trading partners.Design/methodology/approachTen countries are selected for empirical examination of dynamic connectedness among Pakistan and its major trading partner’s stock markets. This study utilizes a spillover index approach model and considers daily, weekly and monthly datasets spanning 25 years from 1995 to 2019.FindingsThe results indicate that stock markets provide efficient channels for return and volatility spillovers. Moreover, it is found that the intensity of spillovers during the financial crisis is more intense as these crises are major determinants of contagion; consequently, investors, speculators and policymakers use these events for their respective purposes.Originality/valueResearchers, practitioners, policymakers and investors may all benefit from the findings in areas including risk management, portfolio diversification and trading methods.

The dynamic volatility connectedness of global financial assets during the Ebola & MERS epidemic and the COVID-19 pandemic

ABSTRACT We investigate the dynamic volatility connectedness of regional stocks, gold, Bitcoin, oil, and uncertainty index related to infectious diseases for the period from January 2014 to June 2022. We investigate the connectivity during Ebola & MERS periods, the normal period, the COVID-19 period and the full sample period. We find that the regional stock indices of the US, Europe, Africa and Latin America are net volatility transmitters whereas regional indices of Asia Pacific, Middle East and North Africa, and other assets like gold, oil and Bitcoin are net volatility recipients throughout the sample periods. By employing the TVP-VAR-based dynamic connectedness approach, we find the temporal evolution of system-wide total connectedness and pair-wise connectedness of financial assets to exhibit higher intensity of volatility spillover during the COVID-19 pandemic as compared to other sub-sample periods. We further observe, based on quantile connectedness approach, that the degree of dynamic connectedness is strong and significant across all the quantile spectrums only during the COVID-19 period. We observe that the safe haven characteristics of assets like gold, oil and Bitcoin diminish during the COVID-19 period due to strong dynamic connectedness with regional stock indices. Our findings have implications for policymakers, investors and portfolio managers in better risk management during periods of health epidemics and pandemics.

What is mine is yours: Sovereign risk transmission during the European debt crisis

We develop an empirical network model to characterize the density of bilateral sovereign credit risk spillovers during the European debt crisis. We show that the spillover density is often asymmetric with heavy tails and that its location and shape vary strongly and systematically in relation to published indicators of systemic stress. Using auxiliary panel data models, we show that the intensity of bilateral spillovers is related to the portfolio investment exposures among country pairs. Because our spillover statistics can be updated daily, they represent a valuable supplement to existing weekly and monthly measures of systemic stress.

Research on the Spillover Effect of China’s Carbon Market from the Perspective of Regional Cooperation

After the official launch of China’s unified carbon market, the potential for carbon emission reduction is huge. The pilot regional markets urgently need to be connected with the national carbon market to form a regional synergy and linkage mechanism and further promote the development of a unified carbon market. Spillover effects can be used to analyze the interaction between multiple markets. In this context, this study focuses on the overall spillover relationship among regional carbon trading markets. Using the VAR-GARCH-BEKK model and social network analysis (SNA), this study empirically analyzes the mean spillover effect and volatility spillover effect of regional carbon markets, and it establishes a spillover network between markets. The results show that the spillover effect of China’s regional carbon markets is widespread. Among them, the mean spillover effect is weak, and the impact period is short;. The volatility spillover effect is strong and has various directions; the spillover network connection between regional carbon markets is strong, but the spillover intensity is weak. Spillover effects will spread to the overall carbon market through information spillover paths and risk spillover paths. The stronger spillover effect and the stronger linkage between markets can bring more resource integration and unified supervision. Finally, we put forward policy recommendations, such as improving the carbon market mechanism and enhancing the maturity of carbon market development, increasing the participation and activity of the carbon market to encourage more participants to join the carbon market, improving the institutional system of the carbon market, and effectively supervising the process of information and risk spillover between carbon markets.

Connectedness between travel & tourism tokens, tourism equity, and other assets

We examine the return and volatility connectedness between travel & tourism tokens and other financial assets, including travel and tourism stocks, energy (WTI oil), cryptocurrency (bitcoin) and bonds, gold, the US dollar, using a generalized vector autoregressive framework. Findings show, in normal economic periods, only weak static spillovers between travel & tourism tokens and other assets. However, dynamic analysis reveals increased intensity of spillovers between travel & tourism tokens and other assets during COVID-19. Results are consistent with travel & tourism tokens offering diversification, including surprisingly to equity of the travel and tourism industry, during downturns that particularly impact tourism.

The dynamic spillovers among carbon, fossil energy and electricity markets based on a TVP-VAR-SV method

The linkage among carbon, fossil energy and electricity markets are gradually strengthening, but few studies focus on the dynamics of the spillover intensity and direction, as well as the time lag and periodicity. Using TVP-VAR-SV method and impulse response functions, this paper explores the time lag and periodicity of spillover intensity and direction among carbon, fossil energy and electricity markets. The empirical results show that the intensity and direction of spillovers among carbon, fossil energy and electricity markets are characterized by time-varying asymmetry. Moreover, the duration of the time-varying spillover effect among carbon, fossil energy and electricity markets are basically maintained at three months, and the spillover effect weakens with the passage of time, especially when the lag period is one month, the intensity of spillover reaches the maximum, which indicates that the dynamic spillovers among carbon, fossil energy and electricity markets have obvious time lag and periodicity. Finally, in the short term, the rise in carbon and fossil energy prices will reduce the value of the electricity market. However, in the long run, it may promote the electricity market to speed up the adjustment of energy structure, reduce carbon emissions and improve the value of the electricity market.

The international spillover behaviour of implied volatilities and forecasting ability of spillover indices

ABSTRACT In this study, we investigate the spillover behaviours of implied volatilities and test the forecasting power of spillover indices. By using a Diebold and Yilmaz’s (2012) framework, we capture a better overall picture of the developments of spillover effects and create a series of spillover indices by quantifying the spillover intensity for each rolling time window such that we are able to investigate the forecasting ability of spillover indices on market returns. There are several findings in this study including (1) the spillover indices of implied volatilities not only enable us to describe the spillover effects but also possess strong forecasting power on market returns, (2) implied volatilities which own emotional-related characteristic create stronger spillover mechanisms than spillover of market returns and (3) by observing the time-varying patterns of spillover indices of implied volatilities, we find that the peaks of spillover indices occur during well-known economic events such as the Lehman Brothers’ Bankruptcy and Greek debt restructuring.

Price connectedness and input–output linkages: Evidence from China

In contrast to the aggregate level, prices at the industry level are flexible. Meanwhile, the majority of the studies on price connectedness through production networks have focused on the supply side and direct transmission, with less attention on the demand side and indirect transmission of price. Thus, this study constructs a network model and analyzes the spillover effect of price at the industry level by using China's supply-side producer price index and demand-side consumer price index from 2007 to 2019. The results show that the spillover effect from the supply-side price to the demand-side price is larger than vice versa. As for the other findings, direct input–output linkages between industries increase the intensity of price spillovers, while the aggregate effect of indirect price transmission has a greater influence on price connectedness than the effect of direct price transmission.

The size of good and bad volatility shocks does matter for spillovers

Based on the rationale that the propagation of stock volatility shocks within a system can be affected by the size of shocks, we apply a tail-based approach of spillovers based on the variance decomposition of a quantile vector autoregression model. The analysis involves the decomposition of the realized variance into positive and negative realized semivariances using 5-min data on six major stock market indices from the US, Eurozone, UK, Japan, China, and India for the period February 14, 2000 - September 30, 2021. The results show that the propagation of volatility shocks within the system is not only shaped by the sign of the shocks (good versus bad volatility) but also by the shock size. For each good and bad volatility spillover, we detect a heterogeneity resulting from the difference in the size of spillovers between the upper and middle quantiles and thus reveal a relative intensity effect, as measured by the Relative Intensity of Shock Spillover (RISS) measure which we propose herein. This points to the necessity of going beyond studying spillovers of average shocks and employing tail-based models capable of uncovering the heterogeneous and intensity effects of the shock size. Otherwise, these features remain hidden, leading to suboptimal inferences and policy implications.

Connectedness between Sectors: The Case of the Polish Stock Market before and during COVID-19

This article studies the connectedness between economic sectors of the Polish stock market. The sectors that are considered are the following: banks, basic materials, chemicals, construction, developers, energy, food, and oil and gas. The analysis of the connectedness among sectors is conducted from a statistical and dynamic perspective. Using the time-varying parameter vector autoregression (TVP-VAR) method, the intensity, direction and variation of volatility spillover between the economic sectors are studied. Two samples are analysed, the first one being from 1 January 2013 to 12 December 2019, which corresponds to the period before the pandemic caused by COVID-19, and the second one being from 1 January 2020 to 2 December 2021, which corresponds to the period during the pandemic. A series of results are obtained. First, the connectedness between the economic sectors varies depends on the time. Second, the connectedness between the sectors was stronger during the crisis caused by the outbreak of COVID-19 rather than before the crisis. The volatility of each sector was also primarily due to their own volatility. Thirdly, the banking sector was the main sector with respect to volatility spillover. The results that are obtained are important for making the right decisions regarding financial stability under crisis circumstances, when considering development strategies for some economic sectors but also in portfolio management for performing diversification and risk-mitigation strategies.