In order to diversify a portfolio, find prices, and manage risk, derivatives products are now necessary. There is a lack of understanding of the true influence of derivatives on the behavior of the underlying assets, their volatility consequences, and their pricing as complex instruments. There is a dearth of empirical research on how these instruments impact company risk exposures and inconsistent findings. This study examines corporate derivatives’ impact on stock price exposure and systematic risk in South African non-financial firms. Using a dataset of listed firms from 2013 to 2023, we employ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to assess the effect of derivatives on return volatility and beta, a measure of systematic risk. Additionally, we apply the Generalized Method of Moments (GMM) to address potential endogeneity between firm characteristics and derivatives use. Our findings suggest that firms using derivatives experience lower overall volatility and reduced systematic risk compared to non-users. The results are robust to various control factors, including firm size, leverage, and macroeconomic conditions. This study fills a gap in the literature by focusing on an underrepresented emerging market and provides insights relevant to global risk management practices.

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

China established pilot carbon markets in 2013. In 2020, it set targets for carbon peaking in 2030 and carbon neutrality by 2050. China’s national carbon market officially commenced operations in 2021. Based on the national market and seven pilot markets, this study established the factors influencing carbon trading prices by examining market participants, macroeconomics, energy prices, carbon prices in other markets, etc. Asymmetrical development among the seven pilot cities, for which the study employed a mixed-effects model, was the primary factor impacting carbon prices. The carbon prices in the pilot cities cannot be extrapolated to the entire country. In the national carbon market, where the study employed a multiple regression lag model, the SSE index was positively correlated with carbon prices, whereas the Dow Jones index had no significant effect on carbon prices in terms of macroeconomics. Coal and natural gas prices were negatively correlated with carbon prices, whereas oil prices were positively correlated with energy prices. The EU market prices have a positive correlation with prices in other markets. The significance of this study is that it covers the largest national Emissions Trading System (ETS) in the world and allows for comparing the characteristics of the Chinese market with those of other ETS markets. Additional studies, including more sectors, should be conducted as China’s ETS coverage increases.

The Consumer Price Index (CPI) is a vital gauge of economic performance, reflecting fluctuations in the costs of goods, services, and other commodities essential to consumers. It is a cornerstone measure used to evaluate inflationary trends within an economy. In Saudi Arabia, forecasting the Consumer Price Index (CPI) relies on analyzing CPI data from 2013 to 2020, structured as an annual time series. Through rigorous analysis, the SARMA (0,1,0) (12,0,12) model emerges as the most suitable approach for estimating this dataset. Notably, this model stands out for its ability to accurately capture seasonal variations and autocorrelation patterns inherent in the CPI data. An advantageous feature of the chosen SARMA model is its self-sufficiency, eliminating the need for supplementary models to address outliers or disruptions in the data. Moreover, the residuals produced by the model adhere closely to the fundamental assumptions of least squares principles, underscoring the precision of the estimation process. The fitted SARMA model demonstrates stability, exhibiting minimal deviations from expected trends. This stability enhances its utility in estimating the average prices of goods and services, thus providing valuable insights for policymakers and stakeholders. Utilizing the SARMA (0,1,0) (12,0,12) model enables the projection of future values of the Consumer Price Index (CPI) in Saudi Arabia for the period from June 2020 to June 2021. The model forecasts a consistent upward trajectory in monthly CPI values, reflecting ongoing economic inflationary pressures. In summary, the findings underscore the efficacy of the SARMA model in predicting CPI trends in Saudi Arabia. This model is a valuable tool for policymakers, enabling informed decision-making in response to evolving economic dynamics and facilitating effective policies to address inflationary challenges.