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The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.

To address the issues of interest, the author employs the econometric analyses of Johansen's multivariate cointegration, Granger's causality, and generalized forecast error variance decomposition. This battery of techniques gives the opportunity to examine the nature of both long‐ and short‐run intersectoral relationships in the QE. To augment the robustness of the empirical analysis, daily as well as weekly closing stock price indices for the four sectors of the Qatar Exchange are used, spanning the period from January 2, 2008 up to April 7, 2011.

Based on daily and weekly data, the results of Johansen's multivariate cointegration analysis suggest that the four sector indices of the QE share a long‐term equilibrium relationship. The Granger's causality analysis based on daily and weekly datasets provides clear evidence that the BFI sector seems to be a significant causal factor in regard to the price predictability of the remaining sectors in the short run, and that the SER sector surprisingly seems to have the least influential role. Finally, the results of the generalized forecast error variance decomposition analysis using daily data show that the IND and BFI appear to be the most exogenous sectors, whereas the SER and INS are the most endogenous ones. The results based on weekly data confirm the relative exogeneity of the BFI sector and the relative endogeneity of the SER sector.

The findings of this study hold practical implications for individual and institutional investors alike. The potential gains derived from cross‐sector diversification could be rather limited, given the significant degree of interrelationships found amongst the sector indices of the QE. Moreover, the composition of domestic portfolios based on sector‐level investments should be revisited, particularly after major events. The findings also bring some important insights for policymakers. Given the influential role played by the BFI sector in the Qatari economy, policymakers should design appropriate strategies that curb the spread of unanticipated shocks originating from this sector to its counterparts. Besides, due to the considerable degree of endogeneity of the SER sector, it is essential for policymakers to set up precautionary regulations, with the aim of minimizing its vulnerability to common shocks in turbulent times.

Building upon the extant research and focusing on a relatively unexplored market, the paper represents a pioneer attempt to provide empirical evidence on the interdependence structure amongst the sector‐specific indices of the Qatar Exchange.

The purpose of this paper is to examine the short‐ and long‐run causal relationship between energy consumption and gross domestic product (GDP) of six emerging economies of Asia. The importance of identifying the direction of causality emanates from its relevance in national policy‐making issues regarding energy conservation.

This paper employs co‐integration and vector error correction modeling along with generalized impulse response functions and varience decomposition tests to check the robustness of the findings.

The empirical results show that there exists unidirectional short‐ and long‐run causality running from energy consumption to GDP for China, uni‐directional short‐run causality from output to energy consumption for India, whilst bi‐directional short‐run causality for Thailand. Neutrality between energy consumption and income is found for Indonesia, Malaysia, and Philippines. Both the generalized variance decompositions and impulse response functions confirm the direction of causality.

These findings have important policy implications for the countries concerned. The results suggest that while India may directly initiate energy conservation measures, China and Thailand may opt for a balanced combination of alternative polices.

Many economists and social scientists are claiming that the increased demand for energy from developing countries like China and India is one of the major reasons for the energy price hikes in recent times. In this backdrop, it is justified to search causal relationship between energy consumption and national output (GDP) of some developing countries from Asia. Since the traditional bivariate approach suffers from omitted variable problems, this paper employs a trivariate demand side approach consisting of energy consumption, income and prices.

The purpose of this paper is to investigate volatility spillovers across the interest rate swap markets of the G7 economies, and then the authors investigate whether spillovers of swap markets contain useful information to explain subsequent stock price movements.

This study uses the short- and long-term swap spread volatility of the G7 countries to explore the spillover effects of international swap markets, and then investigates the relationship between swap and stock markets. The authors use the generalized VAR approach suggested by Diebold and Yilmaz (2012) to study spillovers of international swap markets. The Granger-causality tests are employed to examine the linkage of interest rate swap and stock markets.

This paper shows that a moderate spillover effect exists for the short- and long-term swap markets. Moreover, the results show that the short- and long-term swap markets of France and Germany have a larger impact on other countries’ swap markets than that of other countries’ swap markets on the French and German swap markets. Finally, the results indicate that the total volatility spillovers for the long-term swap markets have a larger influence on the total volatility spillover index of stock markets and the global stock market volatility than that of the short-term swap markets.

Prior literature has used impulse response and variance decomposition analyses to investigate international swap markets linkages. However, the results depend on the ordering of variables. This study uses the framework of Diebold and Yilmaz (2012) to overcome the ordering issue, and thus the authors can compute directional spillovers. This paper is the first study to explore the linkage of the total volatility spillover of swap markets and the stock markets.

The purpose of this study is to examine the transmission of volatility between business confidence index and stock market indices in Greece. The country remains the riskiest project in European Union (EU) and previous studies fail to reach an accurate conclusion regarding the direction of this transmission.

The study covers the period from January 2013 to August 2022 in monthly basis where important economic events occur. Considering that these economic events derive strong volatility moments, the authors adopt a new methodology that measures the transmission of volatility with higher precision. This is the generalized spillover analysis by Diebold and Yilmaz (2009, 2012).

The results indicate that Business Confidence Index (BCI) is the main receiver of volatility spillovers in Greece under all aspects of the used methodology. The specificity of the results shows that business activity through a green growth model is what drives investor confidence and then their activities.

Although a handful of studies have considered the transmission of volatility between BCI and stock market indices, this study contributes in several ways. This study focuses on one country (Greece), avoiding the dispersion of the results from the examination of the relationship in several countries. The used country remains the riskiest project in EU even nowadays, while other studies fail to confirm the main direction of volatility spillovers from business confidence to stock returns. This study covers a period that is ignored by previous studies and includes important economic events. In addition, considering that these economic events derive strong volatility moments, a new methodology is adopted in this field of research that measures the transmission of volatility with higher accuracy.

The purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.

The existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.

First, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.

Compared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.

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 aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.

This study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.

The empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.

The conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

The purpose of this paper is to empirically examine return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

The authors make use of an innovative new methodology of capturing spillovers, which is different from what many existing studies use. The authors employ the measures of return spillovers and volatility spillovers of Diebold and Yilmaz (2009, 2012), referred to as spillover indexes. The spillover index facilitates an assessment of the net contribution of one market in the information transmission mechanism of another market.

The empirical results show bi-directional, but weak interdependence between the South African and Nigerian stock markets returns and oil market returns. The results for volatility spillovers show independence of volatilities between Nigeria stock markets and oil markets, while weak bi-directional spillovers were found between South African equity volatilities and oil volatilities. The time-varying total spillover plots for returns and volatilities are broadly similar and show a trend that has been observed in other studies: an increasing trend during the non-crisis period, a burst in the crisis year, a maintained higher level of transmission afterwards.

Existing studies examining spillovers between oil and stock markets have largely ignored Sub-Saharan African markets. A common feature of existing studies is that they have been conducted for two groups of countries: either European and US markets; or Gulf Cooperation Council markets Thus, this study fills this gap in the literature by examining return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

This chapter examines dynamic connectedness among emerging Asian equity markets as well as explores their linkages vis-à-vis other major global markets. We find that international equity markets are tightly integrated. Measuring connectedness based on a generalized Vector Autoregressive (VAR) model, more than half of all total forecast error variance in equity return and volatility shocks come from other markets as opposed to country own shocks. When examining the degree of connectedness over time, we find that international stock markets have become increasingly connected, with a gentle upward trend since the Asian financial crisis (AFC) but with a rapid burst during the global financial crisis (GFC). Despite the growing importance of Asian emerging markets in the world economy, we find that their influence on advanced economies are still relatively small, with no significant increase over time. During the past decade, advanced markets have been consistently net transmitters of shocks while emerging Asian markets act as net receivers. Based on the nature of equity shock spillovers, we also find that advanced countries are still tightly connected among themselves while intraregional connectedness within Asia remains strong. By investigating whether uncertainty plays an important role in explaining the degree of stock market connectedness, we find that economic policy uncertainty (EPU) from the US is an important source of financial shock spillover for the majority of international equity markets. In contrast, US financial market uncertainty as proxied by the VIX index drives equity market spillovers only among advanced economies.

This paper examines the short and long‐term comovements among UK regional property markets over the period 1976‐2001. The markets examined are London, Outer South East, East Anglia, South West, East Midlands, West Midlands, Yorkshire and Humberside, North and North West. Multivariate cointegration procedures, Granger non‐causality tests, level VAR and generalised variance decomposition analyses based on error‐correction and vector autoregressive models are conducted to analyse relationships among these markets. The results indicate that there is a stationary, long‐term relationship and a number of long‐term causal linkages between the various UK property markets. In terms of the percentage of variance explained, other regional markets are generally more important than innovations in a given region, though this is not the case for the Outer South East. The Outer South East market is segmented from the other regional markets, though also extremely influential in explaining forecast variance in these markets. The overall suggestion is that opportunities exist for portfolio diversification in the UK regional property market, and the Outer South East market should be seen as containing valuable information for forecasting performance in the regional markets.