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With growing interdependence between financial markets, the goal of this paper is to examine the dynamic interdependence between corporate equity and public real estate markets for the USA and a select group of seven European developed economies under a cross-country framework in crisis and boom market conditions. Dynamic interdependence is related to four measures of market linkages of “correlation, spillover, connectedness and causality”.

This study adopts a four-step investigation. The authors first estimate “time-varying variance–covariance spillovers and implied correlations” modeled with the bivariate BEKK-MGARCH methods. Second, the methods of Diebold and Yilmaz (2012, 2014) measure the conditional volatility spillover-connectedness effects across the corporate equity and public real estate markets based on a decomposition of the forecast error variance. Third, the authors implement nonlinear bivariate and multivariate causality tests to understand the lead-lag dynamics of the two asset markets' returns, volatilities and net directional volatility connectedness across different sample periods. Finally, the authors conclude the study by providing a portfolio hedging analysis.

The authors find that corporate equity and public real estate are moderately interdependent to the extent that their diversification benefits increases in the longer term. Moreover, the authors find increased corporate equity-public real estate causal dependence of the market groups of the European and international portfolios during the GFC and INTERCRISIS periods. The nonlinear causality test findings indicate that the joint information of asset markets can be a useful source of prediction for future innovation of market risks. Additionally, policy makers may also be able to employ conditional volatility and volatility connectedness as two other measures to manage market stability in the cross-asset market dependence during highly volatile periods.

One major take away from this academic research is since international portfolio investors are not only concerned the long-term price relationship but also the correlation structure and volatility spillover-connectedness, the conditional BEKK modeling, generalized risk connectedness analysis and nonlinear causal dependence explorations from this multi-country study can shed fresh light on the nature of market interdependence and magnitude of volatility connectedness effects in a multi-portfolio framework.

The hedging performance analysis for portfolio diversification and risk management indicates that industrial stocks (“pure” equities) are valuable assets that can improve the hedging performance of a well-diversified corporate equity-public real estate portfolio during crisis periods. For policymakers, the findings provide important information about the nature of causal links and predictability during the crisis and asset-market boom periods. They can then equip with this information to manage and coordinate market stability in cross corporate equity-real estate relationships effectively.

Although traditional research has in general reported at least a moderate degree of relationship between the two asset markets, investors' knowledge of stock-public real estate market linkage is somewhat inadequate and confine mostly to broad stocks (i.e. stocks that are exposed to public real estate influence) in a single-country context. In this paper, the authors examine the interdependence dynamics in a multi-country (multi-portfolio) context. A clear understanding their changing market relationships in a multi-country context is of crucial importance for portfolio investors, financial institutions and policy makers. Moreover, since the authors use an orthogonal stock market index, the authors allow global investors to understand the potential diversification benefits from stock markets that are beyond the public real estate market under different market conditions.

The purpose of this paper is to examine the short‐term stock market interactions between US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan. These six economies along with Japan and Australia have the largest stock exchanges in the Asia‐Pacific region. The importance of the US market to the Asian economies is the prime motivation for a quantitative assessment of its role in this region. The objective of this study is to measure the dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs) (Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China and India) post Asian crisis of 1997 and also to capture the market interactions during the sub‐prime crisis.

The study has employed Granger causality tests and generalized forecast error variance decomposition (FEVD) analysis to analyze the fluctuations in and the extent of short‐term interdependence between the US and Asian economies. VAR model was estimated to run the simulations for FEVD analysis.

The empirical results from FEVD analysis revealed the dominance of US stock market on Asian markets; the USA being a large economy of the world, an important trading partner and major supplier of capital to Asian region. Stock markets of Asia are not immune to the shocks originating in the USA although the effects of shocks vary considerably across markets. Further, an important implication is that major crisis events can influence the relationship among stock markets.

This is one of the first papers in the Asian context examining the interdependence with the US markets. Hence, even though most of the Asian economies went through liberalization, the macroeconomic and financial circumstances were very different before, after and during the process. This motivated the examination of the interactions between US and other Asian markets.

The purpose of this paper is to investigate the short- and long-run dynamic linkages between selected cryptocurrencies, several major world currencies and major equity indices. The results show that despite sharing some common characteristics, the cryptocurrencies do not reveal any short- and long-term stochastic trends with exchange rates and/or equity returns. The dynamics of each cryptocurrency with the Chinese Yuan appears to be more turbulent than that with the other exchange rates. Each cryptocurrency appears to follow its own trend in the global financial market and is independent of the exchange rates or the global stock markets, thus making them suitable for inclusion in global investment portfolios.

The cryptocurrencies examined are Bitcoin, Dash, Ethereum, Monero, Stellar and XRP. In addition, data were collected on major exchange rates with respect to the US dollar, namely, the euro, British pound, Japanese yen and Chinese Yuan. Finally, the following major stock market indices were selected: SP500, DAX, DJIA, CAC, FTSE, NIKKEI, Hang Seng and Shanghai. The study applied vector autoregressive (VAR) model and Engle’s (2002) dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) specification.

First, it was found that cryptocurrencies do not interact with each other because their correlations are weak and do not share a common long-run path; thus they are not cointegrated. Second, impulse response analysis from the VAR models indicate different reactions of each cryptocurrency to both exchange rate and equity shocks and that cryptocurrencies appear to be isolated from market-driven shocks. Third, the ups and downs in the cryptocurrencies’ dynamic conditional correlations (from the DCC-GARCH models) indicate that all cryptocurrencies were susceptible to speculative attacks and market events.

This paper examines the dynamic linkages among the most important cryptocurrencies with major exchange rates and equity markets and, to the best of the authors’ knowledge, is the first paper to do so. Thus, interested market agents would gain valuable insights as to whether this new form of asset might be used for conducting monetary policies and portfolio construction on a global setting.

The paper contributes to the scant literature on the dynamic linkages among major cryptocurrencies and global financial assets. In general, given the differential relationships of each crypto with the equity markets, one could infer that they represent a decent short-run investment vehicle within a well-diversified, global asset portfolio (as they may increase the returns and reduce the overall risk of the portfolio).

Using a data set comprising 16 European office markets provided by the DTZ Research Institute from Q1 2003 to Q4 2013, the purpose of this paper is to measure the strength of the unconditional transmission of volatility in the returns to direct property between 16 European office markets with the objective of determining the degree of unconditional spillover between markets.

To examine volatility spillovers across the 16 office markets, the authors adopted the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) by measuring cross-office market volatility transmission in asset pricing through estimates of several “volatility spillover indices.”

Volatility spillovers are important and time-varying across the leading office markets, with cross-market volatility interaction being bi-directional and of relative endogenous nature for many markets. The London office market is the “volatility leader” and has exerted significant net volatility influence on the other markets. Additionally, the volatility spillovers between business cycle fluctuations and asset market cycle volatilities are linked across some European economies.

Evidence of co-integration among the domestic volatility spillover cycles implies the presence of unobserved common shocks and might not be good news for international investors who pursue diversification strategies in European office real estate markets.

No previous study has addressed formally the measurement and assessment of the nature and intensity of volatility spillovers across direct office markets on such a broad range of European office markets. The relevance of the topic has been even increasing over the previous years as more and more investors seek for flexibility and participation in the investment process and asset management.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

Rapidly changing technological and marketing environments challenge the survival of business organizations. Developing dynamic capability is critical in helping companies respond to today's turbulent environments. Thus, fruitful studies on the antecedents of dynamic capability have been conducted. However, in the context of the supply chain, little is known about the factors that can be harmful to dynamic capability. Drawing on the theory of cooperation and competition, the first purpose of this study is to examine the relationship between independent goal interdependence with suppliers and dynamic capability by focusing on the mediating role of supplier integration. Combining the information processing theory and transaction cost economics with the theory of cooperation and competition, the second purpose of this study is to discuss and test the moderating role of internal integration.

Using a carefully designed questionnaire, a large-scale survey was employed to collect data in China. The senior manager (e.g. president, vice president, chief executive officer [CEO], executive or purchasing manager) of each company was asked to participate in our survey. The final valid sample in our survey consisted of 233 companies. Hierarchical multiple regression statistical analysis and bias-corrected bootstrapping methods were applied to test the correlation, mediation, moderation and moderated mediation relationships between variables.

The authors found that independent goal interdependence negatively influences dynamic capability through frustrating supplier integration. In addition, the moderated mediation model analysis shows that internal integration weakens the positive direct effect of supplier integration on dynamic capability while neutralizing the negative indirect effect of independent goal interdependence on dynamic capability. The theoretical and managerial implications of these results are discussed.

First, starting from the goal interdependence and supply chain management perspectives, this research not only is consistent with remote theoretical research that explains why interdependence among organizations influences the capability to enhance competitive advantage but also incorporates relevant internal and external factors that influence dynamic capability. Second, by proposing an innovative boundary factor – internal integration – this study also contributes to adjusting the predictions of the theory of cooperation and competition. Third, focusing specifically on the negative antecedent of dynamic capability can provide a better understanding of the antecedents that cause companies to have weakened dynamic capability.

The dynamic capabilities theory indicates that uncertain environments necessitate firms’ dynamic capability. This study aims to examine how dynamic capability can be shaped based on cooperative goal interdependence with supply chain partners by focusing on the mediating role of strategic flexibility and the moderating role of human resource flexibility.

Questionnaire surveys were administered to firm presidents, chief executive officers, chief human resources officers (CHOs) and other senior managers at 300 firms located in China. The data collection process was carried out in one wave with multiple sources. Of the firms contacted, the sample in this study consisted of 233 matched “CHO-other top manager” dyads. Structural equation modeling and the bias-corrected bootstrap method were used to test the proposed causal relationships, moderation model, mediation model and moderated mediation model.

Cooperative goal interdependence with both upstream companies and downstream companies was positively related to dynamic capability and strategic flexibility mediated these main effects. Moreover, human resource flexibility moderated the positive direct relationship between strategic flexibility and dynamic capability and the indirect relationships among cooperative goals, strategic flexibility and dynamic capability such that these relationships in companies with high human resource flexibility were stronger than these relationships in companies with low human resource flexibility.

The findings contribute to the literature on dynamic capability by providing empirical evidence regarding the relationships among cooperative goals, strategic flexibility, human resource flexibility and dynamic capability, which enriches the theory of cooperation and competition and suggests a new path to promote dynamic capability.

Study of the interdependence among economies is of considerable importance. This area includes issues such as the increasing importance of regional economic interactions, the effects of economic growth and recession in the advanced economies on emerging market countries, and financial contagion. A wide range of related terms and methodologies are used in the literature of interdependence. The purpose of this paper is to review the major concepts and various measurements of interdependence in financial markets and the real economy, serving as a reference and benchmark for future research on interdependence among specific regional or global economies.

Major measurements of interdependence are reviewed from simple approach to more complicated ones, and strengths and weaknesses of the various measurements of interdependence are discussed.

This paper surveys the various major measurements of interdependence and illustrates how they have been used to address a substantial range of issues.

The paper shows that studies of macroeconomic and financial interdependence use the same types of econometric measurements. The review and critiques of these various types of measures should be of value to those wishing to do research in these areas and also to those wishing to have a better understanding of papers that they read.

We propose a novel dynamic factor model to characterise comovements between returns on securities from different asset classes from different countries. We apply a global-class-country latent factor model and allow time-varying loadings. We are able to separate contagion (asset exposure driven) and excess interdependence (factor volatility driven). Using data from 1999 to 2012, we find evidence of contagion from the US stock market during the 2007–2009 financial crisis, and of excess interdependence during the European debt crisis from May 2010 onwards. Neither contagion nor excess interdependence is found when the average measure of model implied comovements is used.

This paper aims to attempt to re-capture the stock market contagion effect from the US to the BRIC equity markets during the recent global financial crisis in a multivariate framework. Apart from this, the study also identifies optimal portfolio hedging strategies to minimize the underlying portfolio risk during the period undertaken for the purpose of study.

To account for the dynamic interactions, the study uses vector autoregression (p) dynamic conditional correlation (DCC)-asymmetric generalized autoregressive conditional heteroskedastic (1,1) model in a multivariate framework, coupled with a monthly heat map relating to the co-movement between the US and the BRIC equity markets during the period 2007-2009. Finally, by following the studies, Hammoudeh et al. (2010) and Syriopoulos et al. (2015), the time-varying optimal portfolio hedge ratios and weights are computed.

The results report a contagion impact of the US subprime crisis (following the collapse of the Lehman Brothers) on the Indian and Russian stock markets only. On the other hand, a higher degree of interdependence between the US and Brazilian market has been observed. The US and Chinese equity markets indicate a relatively lower level of interdependence among themselves. The optimal hedge ratios are found to be most effective for a portfolio comprising the US and Chinese stocks even during the crisis period. A US investor should invest approximately 30 cents in the Indian market and rest of the 70 cents in the US market in a US$1 portfolio to minimize the portfolio risk without lowering the expected returns. During the crisis period (2007-2009), the optimal portfolio weights indicate a higher weightage to the BRIC stocks.

The results support the construction of optimal US–BRIC stock portfolios and provide an insight to the investors and policy makers both domestic as well as international, with regard to the contagion impact and interdependence, especially during a crisis period.

The study uses a DCC model in a multivariate framework instead of bivariate, wherein all the markets are factored into a single interaction framework across a very long period (2004-2014). Second, a heat map of monthly correlation combinations has been created for the period 2007-2009, to comprehend the contagion impact or interdependence among the markets. Finally, the study ascertains time-varying optimal hedge ratios and portfolio weights for a two asset portfolio, from a US investor viewpoint, making the study first of its kind in all the perspectives.