Several recent studies pointed out to strong links among the most liquid core European natural gas markets. However, the evidence on the integration of less liquid, peripheral and core European markets is scarce. We address this topic by investigating the dynamics of daily natural gas prices quoted at six European hubs located in Germany, Poland, Czechia, Austria, Italy and Spain. We explore to what extend prices in these hubs are driven by price changes in the most liquid, benchmark European hub (TTF, Netherlands), other energy commodity prices (oil and coal) and local natural gas market fundamentals (whether conditions and gas inventories). We find that natural gas markets are driven by predominantly by changes in the benchmark hub as well as deviations from the law of one price. We also show that other energy commodity prices as well as idiosyncratic factors are important in the least squares regression, but not in a more elaborated GARCH model. These results adds to the discussion on the integration of European natural gas markets.

The implementation of sterilization policy has been noted to raise domestic interest rates as it is designed to lower money supply. This occurs due to the inverse relationship between money supply and interest rates, with a rise in interest rates attracting additional inflows into the economy, putting pressure on monetary authorities. The verification of this hypothesis in the Nigerian context formed the motivation for this study. The main purpose of this study is to investigate whether sterilization policy actually raises domestic interest rates in Nigeria. Using a monthly dataset from 2010M1 to 2021M3 and the ARDL estimation technique, total sterilization serves as a proxy for sterilization policy, while the treasury bills rate proxies the domestic interest rate. Findings reveal that sterilization policy has a positive and significant impact on domestic interest rates in both the short-run and long-run. Additionally, money supply negatively affects domestic interest rates in the short-run, while world interest rates have a negative and significant impact on domestic rates in both the short and long runs.

This paper explores the impact of environmental regulations on environmental quality and green growth, utilizing panel data from six ECOWAS economies. The study employs the CSD-PLS framework, incorporating the Dumitrescu-Hurlin’s panel test, covering the period from 2000 to 2020 with quarterly data. The regression model applied to panel data reveals an inverted U-shaped interaction between environmental regulation and environmental destruction in selected ECOWAS economies, indicating the presence of innovation compensation. Additionally, a U-shaped relationship is identified between environmental regulations and green growth, aligning with the Porter hypothesis. The findings suggest that effective environmental protection policies reduce environmental destruction and promote green growth in ECOWAS economies. Supportive environmental protection policies encourage enterprises to develop environmentally friendly technological and business innovations, which mitigates environmental pollutant emissions and energy consumption, fostering environmental sustainability and green growth.

This paper advances high-frequency foreign exchange (FX) market microstructure analysis by adapting Autoregressive Conditional Duration (ACD) models to study intervals between price updates. By treating these updates as random variables within a point process, the models adeptly capture the dynamic structure of conditional durations and retain key information in high-frequency series. These series display properties critical for understanding market behavior and liquidity dynamics. The findings challenge the belief that increased data frequency reduces microstructural relevance, showing it actually improves understanding of market dynamics. This study broadens econometric model applications and offers updated insights into FX market behavior, providing practical information for academics, practitioners, and policymakers. It contributes significantly to the literature and lays a foundation for future research.

We investigate the relationship between the three most popular uncertainty measures with the means of the state-of-the-art connectedness frameworks applied to the time-varying parameters vector autoregression model with stochastic volatility. We find marked increases in uncertainty connectedness during major economic turmoil and hostile events. VIX turns out to be the most forward-looking uncertainty measure that persistentlytransmits shocks to the remaining uncertainty proxies at lower frequencies. In turn, GPR, approximating specific information related to geopolitical risk, transmits shocks to other measures at short-term frequencies, while the EPU index is largely replicating unanticipated movements in the VIX or GPR. We also present implications of these findings for economic modelling.

It is common for a corporate bond to include a call provision that gives the issuing company an option to call, or redeem, the bond at some prespecified set of call prices before the stated maturity date. Since the option is embedded in the bond, it is not traded publicly and thus its value is unknown to bondholders. This study is aimed to price these embedded options and their related bonds, both callable and noncallable, using the binomial method such that the method is set up to approximate the evolution of the short rate. Using reasonable values for the relevant factors and parameters, our results show that the prices of the options and the two types of bonds are noticeably affected by such factors and parameters as the maturity of the bonds, the coupon, the call price, the volatility of the short rate, and the initial short rate.

Abstract There has been a renewed interest in accurately forecasting the price of crude oil and its fluctuations. That said, this paper aims to study whether the price of crude oil in the United States (US) could be predicted using the stock prices of the top information technology companies. To this end, time-series data was collected and pre-processed as needed, and three architectures of computational neural networks were tested: deep neural networks, long-short term memory (LSTM) neural networks, and a combination of convolutional and LSTM neural networks. The findings suggest that LSTM networks are the best architectures to predict the crude oil price. The outcomes of this paper could potentially help in making the oil price prediction mechanism a more tractable task and in assisting decision-makers to improve macroeconomic policies, generate enhanced macroeconomic projections, and better assess macroeconomic risks.

Abstract This paper analyzes the effect of cash flow from corporate tax aggressiveness on corporate investment expenditure in Nigeria and Ghana from 2010 to 2017. The sampled outcome is measured by estimating pooled ordinary least squares, as well as random and fixed effects models. The study uses dynamic models to draw significance because it corrects for endogeneity, cross-sectional dependence, serial correlation, and heteroscedasticity by including instruments that are uncorrelated with the regressors in the underlying routine during estimation. The corporate tax aggressiveness indicators are tax saving, effective tax rate, book-tax difference, and temporary tax difference - with firm size as the control variable. Findings, among others, reveal that tax aggressiveness has a statistically significant influence on corporate investment expenditure in both countries. This provides evidence that tax aggressiveness is positive and that its coefficients are statistically significant to the tax aggressiveness variables; in particular, tax saving and effective tax rate maintained consistent positive and statistically significant relationships to corporate investment expenditure across all model specifications. In other words, an increase in tax saving and effective tax rate boost the total and new investment expenditure in both countries. Other findings show that a large difference between income reported on financial statements and income reported on tax return reduces corporate total and new investment expenditure in both countries. Furthermore, a proportionate increase in investment maintenance expenditure occurs when a book-tax gap changes in Nigeria. This is because managers reduce taxable income in order to increase investment maintenance expenditure. For the control variables, firm size boosts corporate investment expenditure in both countries.

Abstract This paper evaluates the effect of integrated reporting on the firm value of oil and gas companies comparing the two biggest economies in Africa from 2015 to 2018. The study used Tobin’s Q ratio as a proxy to firm value, while integrated reporting was broken down into five capitals of integrated reporting: intellectual capital, human capital, natural capital, social/responsibility capital, and financial capital. Preliminary analyses were conducted, such as descriptive statistics and correlation matrix. In analyzing the data, the study adopted the panel multiple regression method to identify the possible effect of integrated reporting on the firm value of oil and gas companies in Nigeria and South Africa using the Hausman test to choose between fixed and random effects. The result shows that integrated reporting has a significant positive effect on firm values in South Africa and Nigeria. We, therefore, recommend that integrated reporting in Nigeria should be used as a mandatory reporting system because this will encourage stakeholder understanding, instead of trying to source sustainability reports after examining financial statements.

Abstract Artificial intelligence has transformed the forecasting of stock prices and the evaluation of companies. Novel techniques, allowing the real-time processing of large amounts of data, have enabled the use of data on various external factors to improve the forecasting of the company’s value and stock price. Although conventional approaches solely focus on the use of quantitative data, history has shown that news announcements and statements may significantly affect the performance of the stock value of companies. We present an innovative framework for integrating a nonlinear autoregressive network with a natural language processing approach to analyze stock price movements and forecast stock prices. The framework analyzes and processes the company’s financial statements, determining indicative factors and transforming them into categorical parameters which are then integrated into a nonlinear autoregressive network to estimate and forecast the company’s stock price. The analysis of several Saudi companies listed in the Tadawul index affirms the improved estimation of the stock price and the possibility of a more precise prediction of long-term stock price evolution.