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The purpose of the present study is to directly examine the relationship between bilateral exchange rate and stock market index in a bivariate framework during the period of the floating exchange rate regime in Thailand.

The monthly data used in this study are the stock market index or stock prices from the Stock Exchange of Thailand, and the nominal bilateral exchange rate in terms of baht per US dollar from the Bank of Thailand. The period covers July 1997 to June 2010 with 156 observations. This is the period that the country switched from fixed to floating exchange rate regime. The stock market return is calculated by the percentage change of stock market index (or stock prices) while the exchange rate return is the percentage change of the nominal bilateral exchange rate. Three estimation methods are used to capture the interaction between stock and foreign exchange markets: bounds testing for cointegration, non‐causality test, and the two‐step approach with a bivariate GARCH model and Granger causality test.

The results of the present study show that bounds testing for cointegration does not detect the long‐run relationship between stock prices and exchange rate. In addition, the non‐causality test fails the diagnostic test for multivariate normality in the residuals of the estimated VAR model. However, the two‐step approach adequately detects the linkages between the stock and foreign exchange markets. It is found that there exists positive unidirectional causality running from stock market return to exchange rate return. The exchange rate risk causes stock return to fall as expected. Moreover, there are bidirectional causal relations between stock market risk and exchange rate risk, but in different directions.

Since a rising trend in the risk in the foreign exchange market causes stock return to fall, both domestic and foreign investors should be aware of the risk or uncertainty in the foreign exchange market because it can cause their portfolio return to fall. For policymakers, reducing exchange rate risk cannot be done without the associated costs from a rising risk in the stock market.

This study provides an evidence of volatility (or risk) spillovers in stock and foreign exchange markets. In addition, the risk in foreign exchange market that adversely affects return in the stock market is an expected phenomenon under the floating exchange rate regime.

This study aims to investigate the cointegration and causality relationships between Hong Kong’s residential property price and stock price, using quarterly data, from the 1st quarter of 1980 to the 3rd quarter of 2015.

In contrast to other studies, the cointegration test used is the autoregressive distributed lag (ARDL) cointegration (bounds testing) approach of Pesaran et al. (2001) that based on the estimation of an unrestricted error correction model and the causality test is based on non-causality test of Granger et al. (2000). Moreover, this research employs recursive least square procedures and Chow (1960) breakpoint test to detect unknown structural break and variation of relationships between residential property and stock price over the whole sample period.

The results of ARDL cointegration tests running from stock to residential property markets provide strong evidence to support the hypothesis that the stock and residential properties are cointegrated. The results of Granger et al. (2000) non-causality test support the view of wealth effect that stock price has an important causal effect on residential property price in Hong Kong but not vice versa. In addition, the results of recursive ordinary least squares coefficients estimates and Chow (1960) test (breakpoint test) for structural instability confirm the variation of the relationships between stock and residential property markets over the sample period.

The empirical results from cointegration and causality tests suggest that the residential asset returns are better predicted by including the lagged difference values of stock price.

This is the pioneering study to examine the cointegration and causality study of stock and residential property price in Hong Kong by employing Pesaran ARDL cointegration approach and Granger non-causality approach. Investors are able to perform an effective evaluation to assist in allocating investment funds, and the government bodies can implement supplement housing policy in response to the public needs.

The purpose of this paper is to develop a new framework to test the hypothesis that portfolio model predicts a negative correlation between stock prices and exchange rates in a trivariate transmission channel for foreign portfolio equity investment.

This paper utilizes panel data for eight economies to extend the Dumitrescu and Hurlin (2012) Granger non-causality test of heterogeneous panels to a trivariate model by integrating the Toda and Yamamoto (1995) approach to Granger causality.

The evidence suggests that stock prices Granger-cause exchange rates and portfolio equity flows Granger-cause exchange rates. However, the overall panel evidence casts doubt on the explicit trivariate model of portfolio balance model. The study shows that Indonesia may be the only case where stock prices affect exchange rates through portfolio equity flows.

The proposed test does not account for potential asymmetries or structural shifts associated with the crisis period. To isolate the impact of the Asian Financial crisis, this paper rather splits the sample period into two sub-periods: pre- and post-crises. The sample period and countries are also limited due to the use of the balance of payment statistics.

The study casts doubt on the maintained hypothesis of a trivariate transmission channel, as posited by the portfolio model. Policy makers of an economy may integrate capital market and fiscal policies in order to maintain stable exchange rate.

This paper integrates a portfolio equity inflow variable into a single framework with stock price and exchange rate variables. It extends the Dumitrescu and Hurlin’s (2012) bivariate stationary Granger non-causality test in heterogeneous panels to a trivariate setting in the framework of Toda and Yamamoto (1995).

The purpose of this paper is to examine the relationship between financial development indicators and human development in India using annual data from 1980-2012.

The Ng-Perron unit root test is used to check for the order of integration of the variables. The long run relationship and short run dynamics are examined by implementing the ARDL bounds testing approach to co-integration. Granger’s non-causality test and variance decomposition techniques are also used to examine the impact of financial development indicators on human development.

The results confirm a long run relationship among the variables. The results of granger non causality indicate that unidirectional causality runs from financial development indicators to human development index (HDI). The variance decomposition analysis shows that among all the financial indicators, broad money supply (M3) has the largest contribution to changes in human development in India.

The present study recommends for appropriate reforms in financial market to attain sustainable human development in India. The findings will be useful for India’s policy makers, in order to maintain the parallel expansion of financial development and human development.

This paper is first of its kind to empirically examine the casual relationship between financial development indicators and human capital development proxied by HDI in India by using modern econometric techniques.

The purpose of this study is to investigate the dynamic relationship that exists between energy consumption, environmental pollution and per capita economic growth in Tanzania. The energy consumption is represented by electricity usage in kilowatt hours (kWh) per capita, while environmental pollution is represented by carbon emission per metric tons and economic growth by gross domestic product (GDP) per capita.

This investigation is made based on the Environmental Kuznets Curve using time series annual data from 1975 to 2013 by applying the more robust causality technique of Toda and Yamamoto non-Causality test (1995), Impulse response and Variance Decomposition, Augumented and Dickey–Fueller test and Philips and Perron Test of unit root tests.

Economic growth rate (LGDP) and energy consumption per capita (LENGY), both being unidirectional, cause environmental pollution through carbon emission (LCO₂) in Tanzania. Interestingly, after using impulse response, a significant and positive economic growth (GDP per capita) was found due to shocks from electricity per capita (energy consumption) and carbon emission (LCO₂) with time. The Variance Decomposition suggested that the percentage of the variations due to shocks or innovations of economic growth (LGDP) and energy consumption (LENGY) to carbon emission is very high and significant, accounting to 46 and 41 per cent, respectively, in 10 years to come.

The study recommends that, in the future, the relationship be examined using super-exogeneity causality tests that takes into consideration the changes in policy or regime in contrast to Toda and Yamamoto. Furthermore, the addition of other variables such as fixed capital formation and labor force, which were not considered in this study, may result in strong correlation.

The results imply that the government of Tanzania can adopt environment conservation and energy saving policies without affecting its economic growth. As a matter of fact, to put a stop to persistent environmental pollution in Tanzania, the energy saving policy should be put in place rather quickly. It is imperative that the government implements policies and strategies that ensure continuous economic growth without forsaking the environment.

Despite the increase in carbon emissions, energy consumption and economic growth in Tanzania since 2000, to date, no previous work has been done to investigate their multivariate relationship. This is the first study that uses the Toda and Yamamoto non-Causality test, Impulse Response and Variance Decomposition Analysis to investigate a trivariate relationship of the variables mentioned above.

The purpose of this paper is to assess the role of agriculture in economic growth and its interactions with other sectors of the Tunisian economy.

Johansen's multivariate approach is used to study the cointegration of the different sectors of the Tunisian economy and overcome the problem of spurious regression. Special attention is paid to investigate non‐causality between agriculture and other economic sectors.

Empirical results suggest that all Tunisian economic sectors cointegrate and tend to move together. In addition, weak exogeneity for the agricultural sector is rejected and this underlines the fact that the agricultural sector should be considered by policymakers in the analysis of intersector growth. However, in the short run, agriculture in Tunisia seems to have a partial role as a driving force in the growth of other non‐agricultural sectors and agricultural growth may be conducive only to the agro‐food industry sub‐sector. In addition, while Tunisia started improving quality of services and restructuring the banking sector to make it “internationally” viable, this paper's statistical results indicate that the agricultural sector does not fully benefit from the development of the commerce and services sector and the presence of credit market constraints continue to hamper growth of agricultural output in Tunisia.

Although high importance is placed on the agricultural sector, in the context of the Tunisian economy, the issue of agricultural contribution to the economic growth has often been raised by policymakers but rarely examined empirically.

The purpose of this paper is to investigate whether the Australian equity market is informationally efficient in the semi‐strong form with regard to interest rates and the exchange rate shocks during the period 1994‐2006.

There is evidence that the data are non‐normal and that autoregressive conditional heteroskedasticity (ARCH) effects exist and in such circumstances, standard estimation methods are not reliable. A new method introduced by Hacker and Hatemi‐J which is robust to non‐normality and the presence of ARCH is applied.

The results show the Australian equity market is not informationally efficient with regard to either the interest rate or the exchange rate.

The empirical findings, in contrast to several previous studies, imply that the possibility for arbitrage profits in the equity market might exist.

The purpose of this paper is to investigate the relationship between the energy consumption and the economic growth in the USA and in a sectoral level by using monthly data from January 1991 to May 2016.

While assessing the relationship at a country level, the authors also examine five sectors by using quantile causality.

The findings indicate the existence of a causality at the sectoral level in tails. More specifically, industrial and electric sectors cause the growth at the lower and higher levels. Residential, commercial and transportation sectors do not cause the growth in all levels. Total consumption causes the growth in the middle and low levels but not in the high level. Finally, the empirical evidence signifies an asymmetric relationship between the covariates.

The results imply that when the consumption deals conditions with fluctuation, it is likely to be affected by growth. In such a case, energy policies gear toward reducing or increasing energy intensity, improving energy efficiency, encouraging the use of alternative sources and investing in the development of technology.

The authors use, for the first time, the quantile causality for the case of energy consumption and economic growth. The quantile test is useful for a thorough comprehension of the causal relationship for this area. Compared to the OLS, which is used for the majority of causality tests, the quantile investigates the causality at the sectoral level in the tails.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

The study aims to find the synchronization between foreign agriculture investment (FAI) and Sustainable Development Goals (SDGs) related to agriculture as classified by the Food and Agriculture Organization (FAO). The study tries to find such an association in India over 2 decades from 2001.

The Toda-Yamamoto Granger using the M-Wald test for the non-causality procedure is applied to find the synchronization. Stationarity is tested using the Augmented Dickey-Fuller, Phillips-Perron and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests. The Johanson methodology with MacKinnon-Haug-Michelis P-value is employed for the Cointegration test.

The empirical results indicate that the FAI Granger cause SDG2 “Zero hunger” and “Overall sustainability”, but SDG13 “Climate Change”, SDG6 “Clean water and sanitation”, SDG12 “Responsible production and consumption” and SDG15 “Life on Land” granger cause global investments. Notwithstanding, SDG5 “Gender equality” and SDG14 “Life below water” found no-way causality with FAI.

Host governments should prioritize sector-level sustainable development, notably agricultural SDGs, to attract global investments. Foreign agriculture investment is influenced differently by various SDGs; thus, policymakers should concentrate on specific agricultural SDGs to enhance the flow of capital into the agriculture sector. Global investors should take sustainability into account while framing foreign investment plans, and the supra-national organization may consider global agricultural investments while addressing the problems related to global food security.

The distinguishing feature of the study is that SDGs classified by the FAO from a global investment perspective have not been studied so far.