The Relationship Between Shipping Demand and Macroeconomic Determinants in Türkiye: An ARDL Approach

The interaction between oil and stock market returns is one of the most important relationships that have a significant influence on the economy of any country all over the world. Therefore, this paper investigates the impact of crude oil prices on the Chinese stock market and selected industries by using the VAR-DCC-GARCH model over the period from December 26, 2001, to April 30, 2019. The empirical results show that the impact of Brent crude oil prices on the Shanghai Composite Index and selected industries is significant. However, there are some variations in these relationships and the degree of influence on each differs during different sample periods. Brent crude oil prices exert substantial influence on some specific industries, like mining, chemical, nonferrous metals, and steel. Whereas, the volatility spillover effect of Brent crude oil prices is stronger within the mining, chemical, steel, nonferrous metal, building materials, building decoration, electrical equipment, electrical equipment, textile and garment, light manufacturing, public utility, and transportation industries than within other industries. When oil prices change abruptly, the risk of spillover impacts of oil prices on stock markets will also increase. In conclusion, the impact of Brent crude oil prices on the Chinese stock market is generally positive. Furthermore, the subsequent volatility of Chinese stock market prices will, in turn, influence the volatility spillover of Brent crude oil prices on the indexes. The result is an ongoing back and forth of changes in price volatilities.

This study examines the impact of crude oil prices on transportation sector stock returns of Turkey. ARDL Bound Test approach is utilized to investigate both long-run and short-run impacts. Research findings show that crude oil prices have an adverse impact on stock returns in short-run since oil is a crucial input for transportation firms. However, in long-run, increasing oil prices enhance stock returns in the sector. This result can be explained by the oligopolistic market structure of the industry. This study also investigated the impact of other factors on stock returns such as macroeconomic activity, aggregate stock market performance, and global economic policy uncertainty. The results imply that transportation sector returns are also highly sensitive to macroeconomic and aggregate stock market performances. Besides its academic contribution to the literature, the findings of this research offer precious practical implications for financial investors, industry stakeholders, and policymakers as well.

Oil has been one of the primary wellsprings of Indonesia's revenue, either from government spending plan or balance of payments purpose of perspectives. Because of supply and demand of oil on the planet market, prices of oil, either ICP, Brent UK, or WTI, had been decay of late. Oil prices and economic wellbeing are essential markers to see the achievement of Indonesia's improvement execution. The utilization of oil as the world's fundamental energy source when all is said in done and Indonesia specifically is driven by industrialization. The more ventures, the more prominent the energy resources required. In a similar setting, economic wellbeing will likewise expand oil demand. Oil has a strategic nature and is a vital ware that influences the world economy. Both oil exporters and merchants are probably going to feel the impacts of oil price advancements. Oil prices dropped pointedly since June 2014 finishing a four – year time of relative price strength. The size and speed of decay has been noteworthy yet not remarkable. This exploration plans to analyze the impact of crude oil prices on economic wellbeing in Indonesia. Data on crude oil prices and economic growth are yearly time series data stretching from 1987 to 2016. The aftereffects of co-integration tests demonstrate that there is no long-term connection between crude oil prices and economic wellbeing. In any case, the estimation of the autoregressive distributed lag (5.0) model demonstrates that in the short term, there is the impact of crude oil prices toward economic wellbeing. Keywords: Crude oil, oil price, growth, Autoregressive distributed lag model, Indonesia JEL Classifications: E31, A10DOI: https://doi.org/10.32479/ijeep.8311

Research background: The effects of oil price fluctuations on the macroeconomic performance in oil-importing and oil-exporting countries have stimulated considerable research activity. However, the debate is far from being closed. Purpose of the article: This paper revisits the impact of crude oil price on economic activity in the Gulf Cooperation Council oil-exporting countries. The study covers a relatively long period spanning from 1960 to 2018. Methods: The empirical investigation accounts for structural breaks, nonlinearity, and non-normal ?distribution of data. The Kapetanios (2005) structural breaks unit root test?? and ?Saikkonen?Lütkepohl (2000a, b, c) cointegration test with structural shifts are implemented to examine the stationary properties of data and the presence of cointegration between variables, respectively. Moreover, the quantile regression is employed to assess whether the impact of oil price on real GDP differs across different states of the economy. Findings & Value added: Empirical results suggest the absence of long-run cointegrating relationships between oil price and GDP in all countries. The quantile regression reveals that oil price does not affect real GDP in the same way across countries and for different business cycle phases. More specifically, the symmetric quantile regression findings reveal that oil price exerts a positive impact on GDP in all countries and that the effect is higher during the recession than expansion states. The asymmetric quantile regression shows that GDP reacts to positive oil price changes in all countries. However, only the Emirati and Omani GDPs are affected by negative oil price changes.

This study investigates the linear and nonlinear Granger causality relationship between the Baltic Dry Index (BDI) and global economic policy uncertainty (GEPU). There is a unidirectional linear Granger causality relationship running from GEPU to the BDI and no nonlinear Granger causality relationship between the BDI and GEPU. Hence, the Granger causality running from GEPU to the BDI is relatively direct, concise and swift but not very complicated. The linear Granger causality relationship between them is time-varying.

The aim of the research paper is to analyse the impact of crude oil price and exchange rate volatility affect the performance of stock return in India. Auto Regressive Distributed Lag Bound Test Modelhelps to analyse the dynamic relationship between oil prices, exchange rate and stock market return in India during 1995 to 2018.The estimated results suggest that there exist along run co-integrationor relationship between crude oil price, exchange rate and Indian stock market return. The impact of crude oil rice and exchange rate volatility significantly negative contribution to the performance of Indian stock market. The Error Correction Model (ECM) provides a framework for establishing links between the short-run and long-run approaches to econometric modelling. The equilibrium correlation coefficient is estimated -0.85 is highly significant at one percent. This result confirm the existence of bound test. The coefficient of ECM is highly significant with negative sign, which confirms the result of Bound Test for co-integration. In short the speed of adjustment towards long run equilibrium at the rate of 85 percent monthly.

This paper attempts to investigate empirically the dynamic relationship among crude oil price, exchange rate and Indian stock market. Using daily data of Crude oil price, Dollar-Rupee value and Nifty returns from April 2010 to March 2015, correlation, regression and Granger-causality approach in a bi-variate VAR framework has been used to investigate the causality between crude oil and nifty returns; exchange rate and nifty returns. Augmented Dickey Fuller (ADF) test has been used to test whether the data is stationary or not. The outcome of the study was there is a significant negative correlation between nifty returns and exchange rate and significant positive correlation between nifty returns and crude oil, and a unidirectional causality running from nifty returns to exchange rates and crude oil price to nifty returns.

A Study on the Impact of Industrial Production Index (IPI) to Beverage, Food and Tobacco Sector Index with Special Reference to Colombo Stock Exchange

Freight rate index forecasting with Prophet model based on multi-dimensional significant events

The dynamic linkages among crude oil price, climate change and carbon price in China

The objective of this study is to analyze the impact of crude oil prices on the EU-27 agricultural sector in an era when the biofuels sector is expanding because of the policy initiatives taken by the EU Commission and member states. To this end, first a baseline is set up for the EU-27 ethanol, grain, and dried distillers grains markets. In the next step, two different scenarios are run. The first scenario incorporates a $10- per-barrel increase in the EU-27 crude oil price with the ethanol import tariffs in place. The second scenario incorporates the same shock with the ethanol import tariffs removed. In the first scenario, higher crude oil prices increase ethanol consumption, production, and therefore grain prices. In the second scenario, the impact of trade liberalisation is larger than the impact of the higher crude oil price. So, grain prices decline in this scenario despite an expansion in ethanol consumption. If there were a high enough crude oil price shock, which would affect the EU-27 ethanol market more than trade liberalisation, the net impact on grain, feed, and food prices from the crude oil price shock would be mitigated by the increased trade from trade liberalisation. The study shows that the impact of energy prices on the EU-27 agricultural sector is increasing with the emergence of the biofuels sector. It also illustrates the importance of trade policy in responding to higher crude oil and grain prices.

This article uses quantile regression and Granger Causal Relation Test modeling analysis of time series data to find out the price of crude oil influence on the price of coal. Results show that there is asymmetric relationship between the crude oil price and the price of coal, when the coal price is in a low number, the impact of crude oil price on coal price is significant, however with quantile values become larger, the price of crude oil and coal prices gradually decoupling.

This study investigates the impact of various macroeconomic factors on the Karachi Stock Exchange (KSX), focusing particularly on the periods before and after the September 11, 2001, attacks. Time series data analysis techniques are employed, beginning with the Augmented Dickey-Fuller test to assess data stationarity and detect unit roots. The Chow test is utilized to identify structural breaks in the data, revealing significant shifts post-9/11 compared to pre-9/11 periods. Additionally, the Granger Causality test examines causal relationships among the variables. Multiple regression analysis further explores the influence of macroeconomic factors such as crude oil prices, inflation, interest rates, exchange rates, GDP growth, and gold prices on stock prices. Moreover, the results of the study indicate stationary data post-Augmented Dickey-Fuller test, with structural breaks identified by the Chow test aligning with the timing of the 9/11 event. Furthermore, the study finds that macroeconomic variables, including Foreign Direct Investment, price stability, and turnover, added value to the stock market post-9/11. The Granger causality analysis indicates no significant causal relationships between stock prices and the examined variables throughout the study period, affirming data authenticity. The study provides insights valuable to investors and policymakers, emphasizing the impact of crude oil prices and interest rates on stock prices and suggesting strategies for managing market volatility and shaping economic policies in response to geopolitical shocks.

The study aims to test the relationship between investment in human capital and economic growth in Sudan. It assumed that the growth in both of enrolled in different levels of education and government expenditure on education will lead to increase the economic growth in Sudan. The variables were subjected to several econometrics tests, such as Augmented Dickey–Fuller test (ADF), Autoregressive Distributed- lagged Model (ARDL) and the Error Correction Model (ECM) to test the short- and long- term relationship between study variables. The results of long- run parameters of the (ARDL) model showed a positive correlation between real Gross Domestic Product (GDP) growth rate and the percentage of enrollment in universities from the total population. Also it showed a positive correlation between real GDP growth and education expenditure as a percentage of Gross National Income (GNI). While the estimated results of the bounds test for co- integration within the (ARDL) methodology results provided evidence of a long- run equilibrium relationship between the real GDP growth and the enrollment ratios of primary, secondary and university levels to the total population. Also provided evidence for a long- run equilibrium relationship between real GDP growth and the education expenditure as a share of GNI. Export of goods and services as a percentage of GDP and inflation rate. While the results of the estimated error correction model (ECM) confirmed that the real GDP growth is adjusted to its equilibrium value in each time period by 93% and 88%- for the two models respectively- of the remaining balance of the period with onetime lag. The study recommended the expansion of university education to raise the rate of economic growth through the development of policies to encourage investment in it, raise the quality of university education and improve the outputs quality, enhance the curricula to keep pace with the modern technology progress, linking education outputs with the needs of the labor market requirements and raising the percentage of education expenditure from GDP, as well as to encourage the governmental educational institutions to develop and diversify their self- financing resources.

The objective of this study is to examine the short‐run and long‐run impact of macroeconomic variables on E7 stock indices across bullish, bearish and normal states of the stock markets. For this purpose, this study uses both autoregressive distributed lag (ARDL) and quantile ARDL (QARDL) models. The findings based on the ARDL model indicate that, in the long‐run, foreign direct investment (FDI), trade balance and industrial production index (IPI) significantly affect emerging stock indices. In addition, the findings based on the QARDL model indicate that the short‐run effect of FDI, consumer price index, interest rate and exchange rate varies across bullish, bearish and normal states of the emerging stock markets, whereas the long‐run effect varies for all macroeconomic variables except IPI. These findings indicate that the results change when QARDL model is used; however, these findings remain same across seven emerging stock indices. Finally, this study proposes important policy recommendations based on the findings of this study.