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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.

This study investigates the long-run relationship between workers' remittances and human capital formation in Sri Lanka by using the macro-level time series data during the period of 1975–2020.

In this study, the augmented Dickey–Fuller (ADF) and Philips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test, the forecast error variance decomposition technique and impulse response function analysis were employed as the analytical techniques.

In accordance with the results of unit root tests, the variables used in this study are mixed order. Results of cointegration confirm that workers' remittances in Sri Lanka have both long-run and short-run beneficial relationship with human capital formation. The Granger causality test results indicate that there is a two-way causal relationship between workers' remittances and human capital formation. The results of forecast error variance decomposition expose that innovation of workers' remittances contributes to the forecast error variance in human capital in bell shape. Further, the empirical evidence of impulse response function analysis reveals that a positive standard deviation shock to workers' remittances has an immediate significant positive impact on human capital formation in Sri Lanka for a period of up to ten years.

This research provides insights into the workers' remittances in human capital formation in Sri Lanka. The findings of this study provides evidence that workers' remittances help to produce human capital formation.

By using the ARDL Bounds cointegration and other techniques in Sri Lanka, this study fills an important gap in academic literature.

The author shows that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlights under which conditions. In doing so, the author generalizes the Pesaran and Timmermann (2005)’s forecast error decomposition and shows that it depends on four terms: (1) a period ahead risk; (2) a bias due to a conditional mean shift; (3) a bias due to a variance mismatch; (4) a gap term valid only conditionally. The author also derives new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, the author introduces new simulation-based estimators for the finite sample forecast properties which are based on the derived decomposition. The author’s finding points out that, in some cases, parameter instability can be neglected by extending the window backward and forecasters can be insured against higher forecast risk under this model class as well, generalizing Pesaran and Timmermann (2005)’s result. The author’s result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.

This paper aims to study the co-integration among the stock markets of BRIC nations of Brazil, Russia, Indian and China to analyze if the series move apart or they move together in the long term. and to examine the implied volatility transmission between the Indian implied volatility index and three international indices and vice-versa by using synchronized daily data by using techniques such as generalized impulse response functions and variance decompositions. More specifically, the authors investigate how shock to one volatility index affects another volatility index and what is the magnitude and sign of affect and how long does the effect persist?

Unit root tests are conducted to determine the order of integration for each index. The cointegration analysis is used to evaluate the co-movement of a long-term equilibrium relationship among the four stock market indices. Variance decomposition test helps to explain that how much movement in the dependent variable is explained due to its own shock vis-a-vis to the shock of other variables under the study. Impulse response function is used to find out the impact of the standard deviation of shock given to one variable on the impact on the other variable.

There exists one long-run cointegrating relationship between the four stock markets under study. The coefficient of VECM is −0.00031 which is negative and highly significant at 1 per cent. This confirms the existence of a stable long-run causal relationship between the variables. Variance decomposition shows that indices of Brazil, China and Russia can explain on average 4, 0.5 and 5 per cent, respectively, of the forecast error variance of Indian index. On the other hand, Indian market can explain on an average 6.7, 5 and 3 per cent of the forecast error of Brazilian, Chinese and Russian markets, respectively.

The research paper is an original work of the author.

The purpose of this paper is to investigate the influence of the real interest rates, inflation and risk premium on the time deposit rates of banks in the dual banking system in Malaysia.

The data consists of 1-, 6- and 12-month average time deposit rates of conventional and Islamic banks over the period of January 2000 to June 2017. The cointegration methodologies are used to explore links between the time deposit rates, real rates, inflation and risk premium. The causality tests to test causality linkages between pairs of variables are also applied. The generalised forecast error variance decomposition based on the error correction model is conducted to analyse the impact of variables variation on the deposit rates.

The results show the presence of two cointegration vectors in the deposit rates, real rates, inflation and risk premium, for both conventional and Islamic bank rates. Causality tests reveal that deposit rates are caused by inflation and risk premium in a one-way causality. The results of variance decomposition highlight the importance of inflation and risk premium in explaining the variations in the bank deposit rates. For the conventional bank, inflation shocks play the most important role in explaining the movements of the deposit rates. In Islamic banks, the major determinant’s largest influence is the risk premium. Between the two bank rates, Islamic bank rates receive more influence from the explanatory variables in the long-run compared to conventional bank rates. The real rates have no noticeable effect on the variance of time deposit rates for both banks.

This study presents new evidence on the relationship between time deposit rates and the three explanatory variables, which are the real interest rates, inflation and risk premium, for both conventional and Islamic banks in Malaysia. The dual banking system allows exploring the similarities and differences between conventional and Islamic banks in Malaysia in terms of the linkages between the variables.

This paper aims to explore the impact of the coronavirus on the volatility spillovers of 10 selected developed markets hit by this pandemic (e.g. the USA, Canada, Korea, Japan, the UK, Germany, Italy, Spain, France and China).

The database consists of daily data from January 1, 2020, to December 31, 2022. The data used are the precise daily closing prices of various indices of selected markets gathered from the DataStream and Investing.com databases. The authors use the VAR model to study the transmission of volatility between stock markets and analyze the dynamic links between them. Then, the Granger causality test is used to study the volatility movements and determine which of these markets is likely to influence the others. Then, impulse response functions are used to understand the reactions of the studied markets following shocks in the two most important markets, namely, the American and Chinese markets. Finally, forecast errors variance decomposition is used to measure the dynamic interactions that characterize the relationships between the studied markets.

Empirical results reveal instability in the returns of various indexes and the existence of causal relationships between standardized volatility of markets. The reactions of some markets following a shock in American and Chinese markets differ among markets. The empirical results also show that forecast errors variance of some markets begin coming from their own innovations during first periods. These shares decrease then in favor of other markets interventions.

The findings have significant practical implications for governments around the world as well as for financial investors. The successful practice of China’s pandemic prevention and control efforts may inspire governments to determine how to overcome panic and strengthen confidence in victory. Policymakers can use the insights from our study to design more effective economic policies and regulations to mitigate the negative impact of future pandemics on the financial system. Regulators can use these results to identify areas of weakness in the financial system and take proactive measures to address them. Financial investors may use the outcomes of our result to better understand the impact of global pandemics on financial markets. They may know which markets are the most active, which ones are causing considerable effects on the others and which ones show resilience and an anti-risk capacity. This may help them to make appropriate decisions about their investments.

It has become imperative to estimate the impact of this pandemic on the behavior of financial markets to prevent the deterioration and dysfunction of the global financial system. The findings have important implications for financial investors and governments who should know which markets are the most shaken, which cause remarkable effects on others and which show resilience and anti-risk capacity. Countries could follow China in some measures taken to moderate the negative effects of this epidemic on national economies.

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013.

First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles.

Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks.

Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets.

This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

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.

The demand for money is an important function of stabilization policies where such policies depend on the ability to manipulate the size of money supply in order to insulate real output from monetary disturbances. This paper investigates whether foreign money in Egypt should be included in transactions oriented measures of money supply. Variance decompositions analysis of demand functions for domestic money reveals that deviation of the expected rate of return on foreign money from that on domestic money is more influential than expected depreciation in accounting for quarterly forecast error variance in domestic real balances. This result suggests that portfolio rather than transactions considerations is the dominant factor behind holding foreign money in Egypt. The main policy implication contained in these results implies that foreign money should not be included in transactions oriented measures of money supply that are used as targets when implementing a monetary policy.