Purpose The purpose of this paper is to assess the dynamic impact of financial inclusion on economic growth for a large number of developed and developing countries. Design/methodology/approach This study uses some panel data models such as country-fixed effect, random effect and time fixed effect regressions, panel cointegration, and panel causality tests to examine the linkage between financial inclusion and economic growth. Panel cointegration is being used to test the long run association between financial inclusion and economic growth, whereas panel causality test is used to find the direction of causality between financial inclusion and economic growth. The data on financial inclusion are taken from Sarma (2012) for the period 2004-2010. Findings The empirical findings reveal that there is a positive and long run relationship between financial inclusion and economic growth across 31 countries in the world. Further, panel causality test shows a bi-directional causality between financial inclusion and economic growth Thus, the study confirms that financial inclusion is one of the main drivers of economic growth. Research limitations/implications This study has two limitations. First, this study considers only banking institutions in the analysis. Second, the period tested for the long run relationship is not long enough. Practical implications This study empirically measures the quantitative impact of financial inclusion policies pursued across the world. The study also suggests that policies emphasizing financial sector reforms in general and promoting financial inclusion in particular shall result in higher economic growth in the long run. Originality/value This study attempts to assess the long run relationship between financial inclusion and economic growth with the help of a multidimensional index of financial inclusion. Therefore, this can be a valuable contribution to the banks and policymakers.

Purpose – The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets. Design/methodology/approach – The study has used four futures and spot indices of Multi-Commodity Exchange, Mumbai. The study also employs vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) to analyze the price discovery and volatility spillovers in Indian spot-futures commodity market. Findings – The VECM shows that agriculture future price index (LAGRIFP), energy future price index (LENERGYFP) and aggregate commodity index (LCOMDEXFP) effectively serve the price discovery function in the spot market implying that there is a flow of information from future to spot commodity markets but the reverse causality does not exist. There is no cointegrating relationship between metal future price index (LMETALFP) and metal spot price index (LMETALSP). Besides the bivariate EGARCH model indicates that although the innovations in one market can predict the volatility in another market, the volatility spillovers from future to the spot market are dominant in the case of LENERGY and LCOMDEX index while LAGRISP acts as a source of volatility toward the agri-futures market. Research limitations/implications – The results are aggregate in nature. Further study at disaggregated level will provide further insights on behavior of specific commodity prices and the price discovery process. Originality/value – The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.

Purpose -The purpose of this paper is to construct a robust macroeconomic performance (MEP) index of the State economies of an emerging market economy, i.e. India. Design/methodology/approach -Two variants of data envelopment analysis (DEA) modelsradial and non-radial -are proposed to construct the macroeconomic policy performance of 22 Indian State economies in the post-economic reforms era covering the period : 1994-1995 to 2001-2002, using three macroeconomic indicators: growth in gross state domestic product, price stability, and fiscal deficit. Findings -The authors' three broad empirical findings are: first, the radial and non-radial DEA models yield significantly different rankings of State economies in terms of their MEP index scores; second, as against the use of only growth in gross state domestic product and price stability for MEP measure, the inclusion of fiscal deficit as an additional indicator yields a noticeable improvement not only in the State MEP index scores, but also in their rankings, thus providing the evidence of relatively successful attempt by the Indian States in reducing fiscal deficit, in general, and legislating FRBM bill, in particular; and third, a positive significant correlation between foreign direct investment (FDI) and MEP indicates that a State's overall macroeconomic policy performance does matter to attract FDI. Research limitations/implications -Since the DEA models employed in this study ignore the possibility of asymmetric shocks, the MEP results might be questioned in this deterministic setting. However, the study period has been smooth and has not been subject to any major changes in the State economic policies. Therefore, the MEP results might not be susceptible such changes. However, further research is desired on examining the macroeconomic policy performance behavior of Indian States using bootstrapping DEA. Originality/value -None of the past Indian studies were able to give a comprehensive picture concerning the MEP behavior of Indian State economies, since the methodologies adopted in those studies were not suitable to take into consideration all the macro indicators at a time. Therefore, this present study is considered the first of its kind in assessing the MEP index of the Indian State economies by simultaneously considering all the macro indicators.

Purpose The purpose of this paper is to examine the price–volume relationship in the bitcoin market to validate near-stock properties of bitcoin. Design/methodology/approach Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010–April 16, 2017. Linear and non-linear causality tests are employed to examine price–volume relationship in the bitcoin market. Findings The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns. Research limitations/implications This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the price–volume relationship of bitcoin and its near-stock properties. Practical implications These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investors’ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular. Originality/value The study examines bitcoin’s near-stock properties in a price–volume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authors’ knowledge remains unexplored.

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