Relationship between Bank Credit and Economic Growth: Evidence from Jordan

The integration of digital financial technology has revolutionized the global financial system, driving financial inclusion as an important pillar of sustainable economic development. This study examines the multidimensional effects of Digital Financial Technology and Financial Inclusion on Economic Development in middle- and high-income countries. The study employs various indicators of financial inclusion and technology, namely access to the internet, Automated Teller Machines (ATMs), bank branches, and the number of depositors examined using panel regression analysis covering 20 middle-income countries and 22 high-income countries from 2010 to 2021.The regression analysis results show that ATMs, internet access, bank branches, and the number of depositors all have a positive correlation with the Index of Human Development, which was used to measure economic development. This supports the idea that wider use of technology and increased financial inclusion can lead to higher levels of human development. Conversely, the study highlights a negative correlation between inflation rates (as a control variable) and Human Development Index (HDI) emphasizing the significance of maintaining price stability for sustained economic progress.The study concludes that digital financial technology and financial inclusion positively impact the economic development of countries and the disparity between middle- and high-income countries. So, the middle-income countries should prioritize the development of financial technology and policies to promote financial inclusion. AcknowledgmentThe Deanship of Scientific Research and Graduate Studies at Philadelphia University and Middle East University support this paper.

The research aims at analyzing the causal relationship among Foreign Direct Investment, Domestic saving and Economic Growth in Jordan during the period (1975–2013). The Co-integration Test of Johansen shows that foreign direct investment and domestic saving are in the long run relationship with real income growth in Jordan. Whereas, the impact of foreign direct investment on economic growth is statically significant while the impact of domestic saving is not statistically significant on economic growth in the long run in Jordan. The results of error correction model show that: ECT (Error Correction Term) is 5.5619%, negative and statistically significant at ?= 1% which means that the short run values of GDP converge it's long run equilibrium level by 5.5619% speed of adjustment every year by contribution of Foreign Direct Investment (FDI) and Domestic saving (DS) in Jordan. Granger Causality Test show that foreign direct investment in Jordan is output and saving driven which means that if income and saving increases, Jordan will attract more foreign direct investment.

This study came to inspect the impact of the development of both financial market and banking system on the economic growth of Jordan based on the annual data covering the period 1993–2017. Through the use of many methodologies: Johansen cointegration test, (VECM), and Granger causality test, where real GDP was used as an indicator of economic growth, the real market value of stocks (Market Capitalization) (LCAP) and Share Turnover (LTURN) are indicators for the financial market, Money supply in the broad concept (LM2), and Local domestic credit (LCR) are indicators for the banking sector.The results of this study reported that the study variables are stationary, and in the level of order 2, they are integrated, and a long-run relationship between the study variables existed according to the Johansen co-integration test. VECM model result and the target model result confirm a short-run causality running from the all variables toward GDP. Granger causality test underline a single directional causality running from variables of our study to GDP and denote the short-run impact between LCAP, LTURN, LM2, LCR, and LGDP. The analysis of the variance decomposition shows that the development of the banking system affects economic growth almost equally with the impact of the development of the financial market. The results go to the same line of supply-leading hypothesis.

The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.

This paper investigates the empirical relationship between trading volume and conditional volatility using data from Amman Stock Exchange (ASE) within the framework of Mixed Distribution Hypothesis (MDH). Our sample covered 27 securities, which is most active stocks traded for the period span from 2002 to 2012. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) k Exchange model employed in order to test the persistence in the volatility of stock returns. Our results confirm positive and strong relationship between trading volume for individual stocks and conditional volatility of returns. Moreover, the degree of volatility persistence reduced through the process of adding the contemporaneous volume into the conditional variance equation of GARCH model, and this is according to the predictions of the Mixture of Distributions Hypothesis (MDH).