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This study investigates (1) whether democratization drives sovereign credit ratings (SCR) changes (the “democratic advantage”) or whether SCR changes affect democratization, (2) whether the degree of democratization in sub-Saharan African (SSA) countries affects the associations and (3) whether the associations are significantly affected by resource dependence.

This study investigates the effects of SCR changes on democracy in 22 SSA countries over the period of 2000–2020 VEC Granger causality/block exogeneity Wald tests, and impulse responses and variance decomposition analyses with Cholesky ordering and Monte Carlo standard errors in a panel VECM framework.

The full sample impulse responses find that a SCR shock has a long-run detrimental effect on the democracy and political rights but only a short-run positive impact on civil liberties. Among the sub-samples, it is found that the extent of natural resource dependence does not affect the magnitude of SCR shocks on democratization mentioned above but it is found that a SCR shock affects long-run democracy in SSA countries that are relatively more democratic but is more likely to drive democratic deepening in less democratic SSA countries. The full sample variance decompositions further finds that the variance of SCR to a political rights shock outweighs the effects of all the macroeconomic factors, whereas in more diversified SSA countries, the variances of SCR are much greater for democracy and political rights shocks, which suggests that democratization and political rights in diversified SSA economies are severely affected by SCR changes. In the case of the high and low democracy sub-samples, it is found that the variance of SCR in the relatively higher democracy sub-sample is greater than in the low democracy sub-sample.

These results have three implications for democratization in SSA. First, the effect of a SCR change is not a democratically agnostic and impacts political rights to a greater extent than civil liberties. Second, SCR changes have the potential to spark a negative cycle in SSA countries whereby a downgrade leads to a deterioration in socio-political stability coupled with increased financial economic constraints that in turn drive further downgrades and macroeconomic hardship. Finally, SCR changes are potentially detrimental for democracy in more democratic SSA countries but democratically supportive in less democratic SSA countries. Thus, SSA countries that are relatively politically sophisticated are more exposed to the effects of SCR changes, whereas less politically sophisticated SSA countries can proactively shape their SCRs by undertaking political reforms.

This study is the first to examine the associations between SCR and democracy in SSA. This is critical literature for the Africa’s scholarly work given that the debate on unfair rating actions and claims of subjective rating methods is ongoing.

This study aims to establish the dynamic relationship between international crude oil prices and Indian stock prices represented by the Bombay Stock Exchange (BSE) energy index.

Using Johansen’s cointegration test, vector error correction (VEC) model, impulse response function and variance decomposition test the study tries to ascertain the short-term and long-term dynamic association between the oil price shock and the movement of stock price and Granger causality test is applied to find out the nature of causality.

Considering vector autoregression estimation, the present study analyzes the relationship between the variables and tries to make a valid conclusion. The result of the co-integration test exhibits the presence of a long-term association between these two macro-economic variables during the period under study. Also, in the short-run VEC Granger causality result reveals that the movement of international crude oil price significantly influences the Indian stock price.

To get a more robust result the study can be further extended by taking a longer time period with data of shorter time-frequency such as daily or weekly and further by using more sophisticated econometric and statistical tools. Further, the study can be extended to firm-level investigation considering the forward trading concentration with the Indian oil basket.

In today’s globalized era, forecasting of share price movement helps investors in predicting the market and invest accordingly. Through this liquidity of the markets enhance and markets become more active in the global arena.

This study represents fresh findings in the changing time period the linkage between crude oil prices and stock prices which are of value to the academicians, researchers, policymakers, investors, market regulators, etc.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The study aims to examine the appropriateness of the coefficient of elasticity of trading (CET) as a measure of liquidity using Nigerian stock market data. Given that liquidity is multidimensional, the CET is complemented with the popular measure of liquidity, turnover ratio to explore the causal relationship among the CET, turnover ratio and market return to determine their relevance in security valuation. In other words, an attempt is made to examine if either of these two measures of liquidity is a relevant factor in explaining stock market return.

The Toda-Yamamoto version of Granger causality test is applied to two sets of data on the Nigerian Stock Exchange (NSE). The available monthly time series data are from 2008 to 2019 while the annual data are from 1986 to 2018. The Toda-Yamamoto test is preferred because it is more robust to integration and cointegration of the variables.

The results of the Toda-Yamamoto version of the Granger causality test on monthly data reveal no causal relationship between CET and market return, turnover and market return and CET with turnover and market return. These results are consistent with those for several frontier countries reported by Rubio et al. (2005), Hartian and Sitorus (2015), Batten and Vo (2019) and Sterenczak et al. (2020). The results support the conclusion that the Nigerian economy is not fully integrated with the global economy. Market inefficiency due to order imbalances given the nature of the trading system can also explain the reported results. However, the results from annual data do not tally with the monthly results. There is causality running from CET to market return. There is also causality running from turnover to market return. Therefore, both CET and turnover are statistically significant causal predictors of market return. The results from annual data are consistent with those reported by Marozva (2019).

The key limitation is availability of high-frequency transaction-level data to researchers to consider many measures of liquidity that have been employed in developed countries. The research implication is that more researchers will be encouraged to conduct more studies on liquidity and how the study results can drive policy recommendations. The standard asymptotic distribution of underlying the Toda-Yamamoto approach has been found to lead to overrejection.

This study is the first to apply Toda-Yamamoto model on data from Nigeria to investigate the causal relationship between stock market return and liquidity proxied by the CET given the nature of the automated trading system (ATS) in use. The CET is also complemented with the turnover ratio to explore the multidimensional nature of liquidity and its causal relationship with market return. The study is also interpreted as a determination of the integration of Nigeria's economy with the global economy with its implication on investment diversification.

Professionals from the dairy sector commonly believe that the results of Global Dairy Trade (GDT) auctions are a good leading indicator for prices of dairy commodities. The purpose of this paper is to test that hypothesis for prices of key dairy commodities (skimmed milk powder (SMP), whole milk powder (WMP), butter and cheddar) in the main dairy markets (the US, EU and Oceania).

The leading properties of the GDT auctions are investigated using vector error correction models (VECM).

The results show that prices at GDT auctions may be treated as a benchmark for global prices of WMP and SMP as they affect prices in all considered markets. However, in case of EU market the relationship with the GDT is bidirectional. GDT prices reveal some leading properties also in cheddar market, however price relationships in this market are much more complex. In case of butter market, GDT can be regarded as a benchmark only for Oceania.

The results of this paper improve knowledge on price transmission in dairy markets, show the role of the GDT auctions in the price setting process, and thus may help professionals from the dairy sector to formulate their price expectations more precisely.

Despite the fact that many professionals from the dairy sector treat GDT auctions as a benchmark, so far their leading properties have not been scientifically proven.

This paper investigates the structural model of vector autoregression (SVAR) of the interdependent relationship of inflation, monetary policy and Islamic banking variables (RDEP, RFIN, DEP, FIN) in Indonesia. By using monthly data for the period 2001M01-2019M12, the impulse response function (IRF), forecasting error decomposition variation (FEDV) is used to track the impact of Sharīʿah variables on inflation (prices).

This research uses quantitative approach with SVAR model to reveal the problem.

The empirical results of SVAR, the IRF show that policy shocks have a negative impact on all variables in Islamic banking except the equivalent deposit interest rate (RDEP). The impact of both conventional (7DRR) and Sharīʿah (SBIS) policies has a similar pattern. While the transmission of Sharīʿah monetary variables as a policy operational target in influencing inflation is positive. In addition, the FEDV clearly revealed that the variation in the Sharīʿah financial sector was relatively large in monetary policy shocks and their role in influencing prices.

The empirical results of SVAR, the IRF show that policy shocks have a negative impact on all variables in Islamic banking except the equivalent deposit interest rate ‘RDEP’. The impact of both conventional “7DRR” and Sharīʿah “SBIS” policies has a similar pattern. While the transmission of Sharīʿah monetary variables as a policy operational target in influencing inflation is positive. In addition, the FEDV clearly revealed that the variation in the Sharīʿah financial sector was relatively large in monetary policy shocks and their role in influencing prices.

This empirical paper aims to examine the impact of interest rate (IR) and political instability (POLINS) on Palestine's domestic private investment.

A set of econometric techniques of time series data are adopted to meet the study objectives. They include regression analysis, unit root tests, cointegration test, ARDL & Bound tests, VAR test and Granger causality test.

The study's primary results complement the neoclassical approach, which states that the IR is negatively associated with domestic private investment. The empirical results reveal that there is no long-run relationship. Also, there is no causality between domestic investment and lending rates. Accordingly, these findings alert policymakers to draw a series of steps to minimize the IR at a minimum to stimulate investment for improved economic growth and development.

There is still no national currency in Palestine. The Palestinian Monetary Authority (PMA) is advised to set an appropriate ratio of the IR for the currencies-in-circulation in Palestine for boosting investment and economic development.

This paper provides new background information to both policymakers and researchers on the main determinants of investment in Palestine using econometric analysis. Accordingly, this critical issue is required to be examined in Palestine for stimulating investment.