Search results
1 – 10 of 76Purpose: Through globalization, financial markets have become more integrated and their tendency to act together has increased. The majority of the literature states that there is…
Abstract
Purpose: Through globalization, financial markets have become more integrated and their tendency to act together has increased. The majority of the literature states that there is a cointegration between developed and emerging markets. How do positive or negative shocks in developed markets affect emerging markets? And how do positive or negative shocks in emerging markets affect developed markets? For this reason, the aim of the study is to investigate the asymmetric causality relationship between developed and emerging markets with Hatemi-J asymmetric causality test.
Design/methodology/approach: In this study, the Dow Jones Industrial Average (DJIA) index was used to represent developed markets and the Morgan Stanley Capital International (MSCI) Emerging Market Index was used to represent emerging markets. The asymmetric causality relationship between the DJIA Index and the MSCI Emerging Market Index was investigated using monthly data between January 2009 and April 2019. In the first step of the study, the Johansen Cointegration Test was used to determine whether there is a cointegration between the markets. In the next step, the Hatemi-J asymmetric causality test was applied to see the asymmetric causality relationship between the markets.
Findings: There is a weak correlation between developed and emerging markets. This result is important for international investors who want to diversify their portfolios. As a result of the Johansen Cointegration Test, it was found that there is a long-term relationship between the MSCI Emerging Market Index and the DJIA Index. Therefore, investors who make long-term investment plans should not forget that these markets act together and take into account the causal relationship between them. According to the asymmetric causality test results, a unidirectional causality relationship from the MSCI Emerging Market Index to the DJIA Index was determined. This causality shows that negative shocks in the MSCI Emerging Market Index have positive effects on the DJIA Index.
Originality/value: This study contributes to the literature as it is one of the first studies to examine the asymmetrical relationship between developed and emerging markets. This study is also useful in predicting the short- and long-term relationship between markets. In addition, this study helps investors, portfolio managers, company managers, policymakers, etc., to understand the integration of financial markets.
Details
Keywords
Ishfaq Nazir Khanday, Md. Tarique, Inayat Ullah Wani and Muzffar Hussain Dar
The primary objective of the paper is to examine the asymmetric Cointegration and asymmetric causality between financial development and poverty alleviation on annual data in…
Abstract
Purpose
The primary objective of the paper is to examine the asymmetric Cointegration and asymmetric causality between financial development and poverty alleviation on annual data in Indian context over the period from 1980 to 2019.
Design/methodology/approach
First nonlinearity test by Brooks et al. (1999) is applied to ascertain the nonlinear behavior of the variables used. Once the nonlinear behavior of variables is confirmed, asymmetric and nonlinear unit root tests by Kapetanios and Shin (2008) are applied to check for the order of integration of selected variables. Next, nonlinear autoregressive distributed lag model (NARDL) is employed to analyze the asymmetric Cointegration. Finally, Hatemi-j- asymmetric causality tests is applied to work out the direction of asymmetric causality.
Findings
The empirical findings document the existence of asymmetries in the short-run as well as long-run between poverty and financial development. The asymmetry reveals that negative financial development shocks leave a more profound impact on poverty alleviation than their positive equivalents. The findings of Wald's test also confirm the presence of asymmetric Cointegration. The asymmetric cumulative dynamic multipliers used to examine the behavior of asymmetries and adjustments with respect to time lend credence to the results calculated using NARDL estimator. This result exhibits the robustness of the model. Furthermore, the result emanating from recently introduced asymmetric causality test reveals a unidirectional asymmetric causality between negative shocks in financial development and poverty. The findings of the present study necessitate the need for investigating asymmetric and nonlinear effects in finance–poverty nexus, which existent literature has completely neglected, in order to have relevant policy conclusions.
Research limitations/implications
The study used “Per capita consumption expenditure” as a measure for poverty due to lack of continuous time series data on headcount ratio. In future, researchers can extend this study by incorporating headcount ratio as a measure of poverty in their respective works. There is further scope of research on this issue by finding out the impact of formal and informal sources of credit on poverty separately. A panel data study for developing countries over a period of time could further confirm/negate the findings of the present study.
Originality/value
To the best of the authors’ knowledge none of the studies in Indian context has scrutinized asymmetric and nonlinear impact of financial development on poverty. To dredge up asymmetric structures at work, the authors have used the highly celebrated NARDL estimator. To enrich the existent body of knowledge along the lines of asymmetric (nonlinear) linkages, the authors have also used recently introduced asymmetric causality test by Hatemi-j-(2012) to find out the direction asymmetric causality.
Details
Keywords
Arun Kumar Giri, Geetilaxmi Mohapatra and Byomakesh Debata
The main purpose of the present research is to analyze the relationship between technological development, financial development and economic growth in India in a non-linear and…
Abstract
Purpose
The main purpose of the present research is to analyze the relationship between technological development, financial development and economic growth in India in a non-linear and asymmetric framework.
Design/methodology/approach
The study employs the nonlinear autoregressive distributed lags model (NARDL) and Hetemi J asymmetric causality tests to explore nonlinearities in the dynamic interaction among the variables. The stationarity properties of data are checked by using Ng–Perron and ADF structural break unit root tests. The unit root test confirms that the variables are non-stationarity in level and are differenced stationary.
Findings
The study finds that there is a cointegrating relationship between technological development, financial development and economic growth in the long run. The findings suggest that a positive shock in technological development increases economic growth (coefficient value 1.497 at 1% significance level) and a negative shock will harm economic performance (coefficient value −0.519 at 1% significance level). A long-term positives shock in financial development boosts the economy (coefficient value 1.08 at 5% significance level) and negative shock hampers the economic performance (coefficient value −1.09 at 5% significance level). The asymmetric causality test result confirms bi-directional causality between technological development and economic growth and unidirectional causality from negative economic growth to negative technological development and bi-directional causality between economic growth and financial development, unidirectional negative financial development to economic growth.
Research limitations/implications
The results of this research can significantly facilitate stakeholders and policymakers in devising short-term as well as long-term policies for financial development and technological innovation to achieve sustainable long-run economic growth in India.
Originality/value
This paper is the first of its kind to empirically examine the cointegrating and causal relationship between technology, financial development and economic growth in India using non-linear asymmetric cointegration and causality tests.
Details
Keywords
Shruti Shastri, Geetilaxmi Mohapatra and A.K. Giri
The purpose of this paper is to examine the nexus among economic growth, nonrenewable energy consumption and renewable energy consumption in India over the period 1971-2017.
Abstract
Purpose
The purpose of this paper is to examine the nexus among economic growth, nonrenewable energy consumption and renewable energy consumption in India over the period 1971-2017.
Design/methodology/approach
This study uses nonlinear autoregressive distributed lags model and asymmetric causality test to explore nonlinearities in the dynamic interaction among the variables.
Findings
The findings indicate that the impact of nonrenewable energy consumption and renewable energy consumption on the economic growth is asymmetric in both long run and short run. In long run, a positive shock in nonrenewable energy consumption and renewable energy consumption exerts a positive impact on growth. However, the negative shocks in nonrenewable energy consumption produce larger negative effects on the growth. The results of nonlinear causality test indicate a unidirectional causality from nonrenewable energy consumption and renewable energy consumption to economic growth and thus support “growth hypothesis” in context of India.
Practical implications
The findings imply that policy measures to discourage nonrenewable energy consumption may produce deflationary effects on economic growth in India. Further, the findings demonstrate the potential role of renewable energy consumption in promoting economic growth.
Originality/value
To the best of the authors’ knowledge, this study is the first attempt to explore nonlinearities in the relationship between economic growth and the components of energy consumption in terms of renewable and nonrenewable energy consumption.
Details
Keywords
Alan Mustafa and Abdulnasser Hatemi-J
In this study, a tool has been designed and developed for learning about the concept of lag order within a dynamic model, which can be used in any teaching classes on statistics…
Abstract
In this study, a tool has been designed and developed for learning about the concept of lag order within a dynamic model, which can be used in any teaching classes on statistics and financial data computation. To show a solution for a complex and multi-step process of finding the optimal lag order for multiple variables data series based on an information criterion a module using Visual Basic for Applications (VBA) for Microsoft Excel (MS Excel) is being developed. This module can be used for estimating a multivariate dynamic model as well as determining the optimal lag order of such a model.
Details
Keywords
Alhassan Turay, Mehdi Seraj and Hüseyin Özdeşer
The degree of responsiveness of fiscal and monetary policy mechanisms that promote growth and development in Sierra Leone is the subject of this article.
Abstract
Purpose
The degree of responsiveness of fiscal and monetary policy mechanisms that promote growth and development in Sierra Leone is the subject of this article.
Design/methodology/approach
This article uses both the Auto Regressive Distributed Lag (ARDL) model presented by Hashem and Yongcheol (1998) and the Non-Linear Auto Regressive Distributed Lag (NARDL) model by Shin et al. (2014) to analyze annual time-series data in evaluating the asymmetric effect of real gross domestic product (RGDP), inflation, government expenditure and money supply using annual time-series data for 40 observations over the period 1980–2019.
Findings
The augmented Dickey–Fuller unit root test shows that money supply, government spending and consumer price index are integrated at first difference I (1), while RGDP is stationary at level I (0). The results of the NARDL cointegration test indicate that the variables are cointegrated. The study shows that government expenditure is a positive function of both positive and negative changes. Hence, both positive and negative cumulative sum government expenditures improve economic growth but show a relative weak asymmetric effect with the regressand. This study also reveals that inflation is a negative function of both positive and negative changes with asymmetric effect with the dependent variable. This study shows that the positive change of money supply is statistically insignificant in boosting economic growth, while the negative change positively improves economic growth. Conclusively, this article shows that fiscal policy has a greater and more responsive than monetary policy in promoting growth and development in Sierra Leone. The result of the error correction term of the NARDL model shows a high spend of adjustment of 135% from any disequilibrium of GDP imbalance in the economy.
Originality/value
To address the problem of fiscal dominance in Sierra Leone, this study recommends that fiscal and monetary policies should be coordinated simultaneously and to an appropriate extent to achieve the desired outcome in growth and development.
Details
Keywords
This study aims to explore the relationship among energy consumption, real income, financial development and oil prices in Italy over the period 1960-2014.
Abstract
Purpose
This study aims to explore the relationship among energy consumption, real income, financial development and oil prices in Italy over the period 1960-2014.
Design/methodology/approach
Different econometric techniques – such as the General Methods of Moment (GMM) or the AutoRegressive Distributed Lags (ARDL) bounds test – are usually used in the empirical analysis. Moreover, both the Toda and Yamamoto causality tests and the Granger causality tests are applied to the data.
Findings
The results of unit root and stationarity tests show that the variables are non-stationary at levels, but stationary in first-differences form, or I(1). The ARDL bounds F-test reveals an evidence of a long-run relationship among the four variables at 1% significance level. Moreover, an increase in real GDP and oil prices has a significant effect on energy consumption in the long run. The coefficients of estimated error correction term are also negative and statistically significant. In addition, the paper explores the causal relationship between the variables by using a VAR framework, with Toda and Yamamoto but also Granger causality tests, within both multivariate and bivariate systems. The findings indicate that energy consumption is affected by real GDP.
Originality/value
The study also filled the literature gap of applying ARDL technique to examine this relevant issue for Italy.
Details
Keywords
Gülfen Tuna, Vedat Ender Tuna, Mirsariyya Aghalarova and Ahmet Bülent Atasoy
This study aims to reveal new information about the relationship between energy consumption and economic growth for the time-varying causality.
Abstract
Purpose
This study aims to reveal new information about the relationship between energy consumption and economic growth for the time-varying causality.
Design/methodology/approach
Economic growth and renewable and nonrenewable energy consumption data of the G7 countries (Canada, France, Germany, Italy, Japan, the UK and the USA) for the 1980–2016 period were used in the study. The nonasymmetric causality test developed by Hacker and Hatemi-J (2006) and both traditional and time-varying forms of the asymmetric causality test by Hatemi-J (2012) were used as the study method.
Findings
While the study favors feedback hypothesis for renewable energy consumption in the nonasymmetric causality tests in the UK economy, it favors the same hypothesis for nonrenewable energy consumption in the US economy. However, according to the results reported by Hatemi-J (2012), the feedback hypothesis, which is supported for the UK, is supported only in positive shocks, yet not for each period of analysis. Similarly, feedback hypothesis, which is supported in the USA, is supported only in the negative shocks, yet not for each period of analysis.
Originality/value
This study examined that the asymmetric causality relationship between variables can be analyzed in time-varying form. Therefore, whether positive and negative shocks in renewable and nonrenewable energy consumption always provide useful information in estimations about economic growth is analyzed.
Details
Keywords
Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact…
Abstract
Purpose
Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction.
Design/methodology/approach
In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results.
Findings
The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task.
Originality/value
The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.
Details