Interconnected economies: Exploring financial and economic dependencies between GCC and Turkey

SummaryRecent financial disasters have emphasized the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and asymmetric dependence patterns observed in financial time series. In this paper, we develop a new class of flexible copula models where the dependence parameters evolve according to a Markov switching generalized autoregressive score (GAS) dynamics. Maximum likelihood estimation is performed using a two‐step procedure where the second step relies on the expectation–maximization algorithm. The proposed switching GAS copula models are then used to estimate the conditional value at risk and the conditional expected shortfall, measuring the impact on an institution of extreme events affecting another institution or the market. The empirical investigation, conducted on a panel of European regional portfolios, reveals that the proposed model is able to explain and predict the evolution of the systemic risk contributions over the period 1999–2015.

This study proposed a hybrid modelling framework that integrates Random Forest (RF), Vector Error Correction Model (VECM), and Regression Analysis to enhance macroeconomic forecasting in Nigeria. Addressing challenges such as oil price volatility, structural shocks, and sparse high-frequency data, this approach combines RF’s ability to capture non-linear patterns, VECM’s cointegration of non- stationary variables, and Regression’s parametric efficiency through residual correction and ensemble averaging. Using macroeconomic data from 1993–2022, the hybrid model achieved a 23.4% reduction in Mean Absolute Error (MAE) for GDP (from 15.23 to 11.67) and a 28.5% reduction in Root Mean Squared Error (RMSE) (from 20.45 to 14.62), alongside significant improvements for other variables: 17.6% MAE (exchange rate), 15.2% MAE (inflation), 12.1% MAE (unemployment), and 20.3% RMSE (exchange rate), 18.5% RMSE (inflation), 15.6% RMSE (unemployment). The optimized integration weights ( α = 0.61 for RF, β = 0.17 for VECM, γ = 0.23 for RA in GDP forecasting) highlight machine learning’s dominance in modeling non-linearities, while VECM anchors predictions to long-term equilibria and RA stabilizes parametric relationships. Residual correction and ensemble averaging further reduced systematic biases, as evidenced by tighter error distributions. By bridging machine learning and econometrics, this integrated approach provided policymakers with a robust tool for economic stabilization in resource-dependent economies. While data granularity influenced performance, it highlighted its potential for emerging markets facing structural constraints.

The paper has applied time series model to investigate the wholesale and retail price market integration of major pulses (tur, gram, moong, urad, masoor) in five major regions namely north zone (NZ), south zone (SZ), east zone (EZ), west zone (WZ) and north east zone (NEZ) in the country based on their volume of production. The study has shown that there exists a strong cointegration among the wholesale as well as retail prices of these major pulses, although the cointegration varies. In addition to the horizontal cointegration, the vertical cointegration between the wholesale and retail prices of different pulses has also been investigated. Different causal relationships have been found between wholesale and retail prices in these five zones. The application of vector error correction model (VECM) has indicated that all the error correction terms (ECTs) are negative and most of these terms are statistically significant, implying that the system once in dis-equilibrium tries to come back to the equilibrium situation. The study has also used Impulse response analysis which shows that change in wholesale prices of these five pulses in one zone will cause change in wholesale prices in other zones. The paper has concluded that price signals are transmitted across regions indicating that price changes in one zone are consistently related to price changes in other zones and are able to influence the prices in other zones. However, the direction and intensity of price changes may be affected by the dynamic linkages between the demand and supply of pulses. The study has provided an interesting insight for policy makers, and for contributing to improve the information precision to predict the price movements used by marketing operators for their strategies and by policy makers for designing the suitable marketing strategies to bring more efficiency across the markets.

The main purpose and scope of this empirical study is to explore the causal and dynamic linkages of KSE-100 with emerging stock markets of India, China, Hong Kong, Malaysia, Indonesia, Thailand, Turkey, and Brazil, as well as with the developed stock markets of Japan, USA, UK and France for the period of January, 1998 to December, 2008. 132 monthly stock indices observation are taken. Descriptive analysis, correlation analysis, unit root test, co-integration test, vector error correction model, granger causality test, variance decomposition test and impulse response test is used to identify the existence of short run and long run relationships. Empirical results conclude that KSE-100 is a volatile market and have suitable level of returns. Moreover, JCI and BCI have long run relationships with KSE, and SCI have short run relationships to KSE. The equity markets of BSE, SCI, SET, KLSE, JCI, ISE, BCI and Nikkei-225 granger causes to KSE-100 and KSE-100 granger causes to HSI, SET, KLSE, JCI. Further study explores that the major change in KSE-100 is due to its own innovations and other markets have no significant impact on the KSE. The findings conclude that there is a further need of future study to explore the factors of economic integration among these stock markets. The overall study creates awareness to economic and financial decision makers, international investors, regional investors, investment agencies and banks, about the opportunities of portfolio diversification benefits, funds management and trends of the stock market. Key words: Indices, linkages, portfolio diversification, co-integration, vector error correction model, emerging markets, developed markets, economic integration, KSE-100.

The primary objective of this study is to categorize the different forms of economic reliance that unemployed young adults have on their parents and identify the factors contributing to this dependency. The study utilized data from the Youth Panel Survey (YP) conducted by the Korea Employment Information Service and performed a latent class analysis on unemployed young adults receiving financial support from their parents. Based on the reasons for and levels of parental financial support, four distinct types of economic dependency were identified: 'Moderate Living Expense Dependency,' 'High Housing Expense Dependency,' 'Minimal Living Expense Dependency,' and 'Unconditional Dependency.' Each type is differentiated by the specific reasons young adults rely on their parents and the level of financial support they receive. The study further analyzed how sociodemographic characteristics, career maturity, job search preparation, and work values influence these types of economic dependency. The results indicated that the 'High Housing Expense Dependency' type was more common among young adults living in metropolitan areas and those with a university degree or higher. This group tended to receive higher amounts of financial support from their parents for housing costs. On the other hand, the 'Unconditional Dependency' type was prevalent among students and those whose parents had greater wealth. This group exhibited lower levels of career maturity and placed more importance on economic rewards and job security in their work values. Additionally, the study demonstrated that economic dependency among unemployed young adults is closely linked not only to financial factors but also to their career maturity and job search preparation. For example, those categorized under 'Moderate Living Expense Dependency' and 'High Housing Expense Dependency' exhibited lower career maturity but higher levels of job search preparation. This research aims to provide important policy insights by offering an in-depth understanding of the complex reasons behind young adults' financial dependence on their parents and the types of support they receive. The findings will contribute to developing policies that promote the economic independence of young adults and support a smoother transition into the workforce. Furthermore, the study provides foundational data to help reduce the challenges these young adults face during their transition into employment.

In environmental monitoring, multiple spatial variables are often sampled at a geographical location that can depend on each other in complex ways, such as non-linear and non-Gaussian spatial dependence. We propose a new mixture copula model that can capture those complex relationships of spatially correlated multiple variables and predict univariate variables while considering the multivariate spatial relationship. The proposed method is demonstrated using an environmental application and compared with three existing methods. Firstly, improvement in the prediction of individual variables by utilising multivariate spatial copula compares to the existing univariate pair copula method. Secondly, performance in prediction by utilising mixture copula in the multivariate spatial copula framework compares with an existing multivariate spatial copula model that uses a non-linear principal component analysis. Lastly, improvement in the prediction of individual variables by utilising the non-linear non-Gaussian multivariate spatial copula model compares to the linear Gaussian multivariate cokriging model. The results show that the proposed spatial mixture copula model outperforms the existing methods in the cross-validation of actual and predicted values at the sampled locations.

Long and short term dynamic causal transmission amongst international stock markets

BackgroundThe roles of serum lipids on digestive system cancer (DSC) risk were still inconclusive. In this study, we systematically assessed indicative effects of signature lipidomic biomarkers (high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG)) on DSC (oesophagus, stomach, colorectal, liver, gallbladder, and pancreas cancers) risk.MethodsHDL-C, LDL-C, and TG concentration measurements were respectively analyzed with enzyme immunoinhibition, enzymatic selective protection, and GPO-POD methods in AU5800 supplied from Beckman Coulter. The diagnoses of DSCs were coded using International Classification of Diseases, Tenth Revision (ICD-10) codes updated until October 2022 in the UK Biobank (UKB). In this study, we assessed phenotypic association patterns between signature lipidomic biomarkers and DSC risk using restricted cubic splines (RCSs) in multivariable-adjusted Cox proportional hazards regression models. Moreover, linear and nonlinear causal association patterns of signature lipidomic biomarkers with DSC risk were determined by linear and nonlinear Mendelian randomization (MR) analyses.ResultsA median follow-up time of 11.8 years was recorded for 319,568 participants including 6916 DSC cases. A suggestive independent nonlinear phenotypic association was observed between LDL-C concentration and stomach cancer risk (Pnonlinearity < 0.05, Poverall < 0.05). Meanwhile, a remarkable independent linear negative phenotypic association was demonstrated between HDL-C concentration and stomach cancer risk (Pnonlinearity > 0.05, Poverall < 0.008 (0.05/6 outcomes, Bonferroni-adjusted P)), and suggestive independent linear positive associations were observed between HDL-C concentration and colorectal cancer risk, and between TG concentration and gallbladder cancer risk (Pnonlinearity > 0.05, Poverall < 0.05). Furthermore, based on nonlinear and linear MR-based evidences, we observed an suggestive independent negative causal association (hazard ratio (HR) per 1 mmol/L increase: 0.340 (0.137-0.843), P = 0.020) between LDL-C and stomach cancer risk without a nonlinear pattern (Quadratic P = 0.901, Cochran Q P = 0.434). Meanwhile, subgroup and stratified MR analyses both supported the category of LDL-C ≥ 4.1 mmol/L was suggestively protective against stomach cancer risk, especially among female participants (HR: 0.789 (0.637-0.977), P = 0.030) and participants aged 60 years or older (HR: 0.786 (0.638-0.969), P = 0.024), and the category of TG ≥ 2.2 mmol/L concluded to be a suggestive risk factor for gallbladder cancer risk in male participants (HR: 1.447 (1.020-2.052), P = 0.038) and participants aged 60 years or older (HR: 1.264 (1.003-1.593), P = 0.047).ConclusionsOur findings confirmed indicative roles of signature lipidomic biomarkers on DSC risk, notably detecting suggestive evidences for a protective effect of high LDL-C concentration on stomach cancer risk, and a detrimental effect of high TG concentration on gallbladder cancer risk among given participants.

Purpose: The aim of the study was to analyze the impact of reconstruction policies on Iraq's regional relations post-ISIS. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Iraq’s reconstruction policies post-ISIS have shaped regional relations through reliance on international aid, resource disputes, economic dependencies, and sectarian divisions. Inefficient aid utilization and governance challenges have strained donor trust and regional diplomacy. Resource conflicts with Iran and Turkey, alongside border tensions and economic reliance on Gulf states, highlight Iraq’s need for cohesive policies to foster stability and regional collaboration. Unique Contribution to Theory, Practice and Policy: Neorealism (Structural Realism) Theory, Dependency Theory and Constructivism Theory in International Relations maybe used to anchor future studies on the impact of reconstruction policies on Iraq's regional relations post-ISIS. Iraq should adopt practices that bridge sectarian divides, ensuring equitable resource distribution and fostering national unity. Iraq must also develop policies that reduce dependency on oil revenues and international loans. Strengthening non-oil sectors, particularly through partnerships beyond the Gulf Cooperation Council (GCC), will enhance economic resilience and autonomy.

Energy consumption is a crucial factor to promote industrial sector contribution in an economy for its economic progression. Indeed, Pakistan is an emerging country, but recently adjoining with a very severe deficit of electricity sources. Hence, the industry value added growth leading to economic progression is also fronting inevitable challenges to promote the industry growth. The main objective of the study is to investigate the linkages between industrial sector oil, gas and electricity consumption, and renewable energy consumption with economic development in Pakistan. The findings display evidence of cointegration and a long-run relationship between the consumption of industrial energy and economic growth in Pakistan. The results showed that industrial electricity consumption and industrial gas consumption have a positive and statistically significant impact on economic growth both in the long run and the short run in Pakistan. Industrial oil consumption negatively impacts economic growth in the long run, but positively and statistically significantly impacts economic growth in the short run in Pakistan. Moreover, indications through the vector error correction model (VECM) model confirmed bi-directional relationships of industrial sector oil consumption and economic growth in Pakistan. Furthermore, the uni-directional nexus instituted between economic growth to industrial electricity consumption, industrial gas consumption to industrial electricity consumption, and industrial oil consumption to industrial electricity consumption. The findings uncovered solid interconnections among the studied variables and suggested that the Pakistani government should build a robust policy to diminish the oil, gas, and fossil fuels consumption for electricity production, as a replacement to depend on solar, hydro, wind, and biomass energy sources in Pakistan. Consequently, the government should promote more gas concentrated projects, as these will alleviate the contests of gas dearth and provide it to the industry at cheap prices with ease.

This study explores the short and long term dynamic relationship between macroeconomic variables and stock returns (KSE) for the period from January 1999 to December 2008. Macroeconomic variables include money supply, consumer price index, treasury bills rates, exchange rate, industrial production and reserves. The time series data have been used to examine by employing Johansen and Juselius multivariate cointegration, bivariate cointegration and Granger causality which indicates long term relationship among money supply, consumer price index and industrial production. Granger Causality test provides evidence about lead lag unidirectional relationship between macroeconomic variables and stock returns (KSE). Vector error correction model explores the short term dynamic negative significant relationship among interest rate, exchange rate and also inflation on Karachi Stock Exchange. Money supply has a positive impact, creates the liquidity and accepts the null hypothesis of positive impact on equity market. Variance decomposition test determined that macroeconomic variables are an important source of volatility for the Karachi Stock Exchange. The contribution of this research is used to identify macroeconomic variables that are considerable factors and determinants of Karachi Stock Exchange movements. It also indicates that policy makers should be more careful and watchful about the sensitivity in designing the monetary policy. Key words: Karachi Stock Exchange (KSE), dynamic, macroeconomic variables, causality, vector error correction model (VECM), cash inflows, present value, co-integration.

The main focus of this paper is to analyze the current political and economic relations between China and Myanmar and to present some answers to the following three questions: How do we study the political economy of China-Myanmar relations in the 21st century? What are the features of the current Sino-Myanmar relations? Finally, in what ways do the present relations of these two states differ from those between 1949 to 2000?The paper concludes that the closer economic entente between China and Myanmar in the 21st century is likely to be based on a pattern of asymmetrical complex interdependence, underpinned by political and economic realism of mutual interests and mutual benefits. This 'marriage of convenience' is likely to persist - despite Myanmar's lingering fear towards China due to its emergence as the rising superpower in Southeast Asia. Myanmar can withhold its autonomy notwithstanding domestic constraints and external pressures from the U.S. the EU and the UN. In short, economic rather than political determinants were the main factors shaping the complex interdependent relationship between China and Myanmar between 2001 to 2012.

Extreme weather events (floods and heatwaves) are becoming more frequent and intense due to climate change, posing significant risks to maternal and child health (MCH). These events interact in complex ways, occurring as compounding hazards (simultaneous or overlapping events), multiple hazards (independent but co-occurring risks), or cascading hazards (where one event triggers or exacerbates another). Understanding these interactions is critical for assessing their full health impacts and improving health system resilience. To date, health-related research has primarily focused on the effects of each hazard individually. This study employs an integrated framework that combines copula models, Bayesian networks, and machine learning approaches to analyse multi-hazard interactions, focusing on Zambia as a case study. Data, including daily rainfall and temperature, MCH-related datasets, utilisation data, and health system performance metrics &amp;#8211; such as antenatal care (ANC), postnatal care (PNC), childhood immunisation, place and mode of delivery, and health service utilisation records &amp;#8211; were obtained from Zambia through the REACH project. Daily rainfall was merged with TAMSAT and ERA5 reanalysis data (weather station data) to identify flood and heatwave events across Zambia from 1981 to 2023. Copula models were used to capture non-linear dependencies between heatwaves and floods; Bayesian networks uncovered causal pathways linking hazards with MCH and utilisation outcomes; and machine learning models (e.g., random forests and neural networks) predicted health impacts and identified critical patterns of hazard-MCH interactions. Intermediate variables, such as demand-side factors (e.g., education, wealth, age, etc.) and supply-side factors (e.g., facility density, health worker density, and healthcare financing), were incorporated to improve causal inference and identify actionable pathways. Marginal distributions for temperature and precipitation extremes were modelled using extreme value theory, while copulas quantified the joint probabilities of simultaneous extremes. Bayesian networks provided insights into cascading effects, such as how flooding damages healthcare infrastructure and exacerbates the impact of heatwaves on MCH services. Machine learning models were then trained to predict MCH outcomes (utilisation rates and counts) based on these multi-hazard interactions, leveraging their capacity to handle complex, non-linear relationships. Key results focus on estimating the level of ANC and PNC service disruption caused by compounding hazards, such as simultaneous floods and heatwaves. There is an urgent need for climate-resilient healthcare systems and targeted interventions to mitigate the risks of interacting with climate extremes on MCH. Such disruptions are anticipated to highlight important predictive factors, including increased rates of preterm births and maternal complications. This integrated approach, combining statistical, causal, and predictive tools, offers a holistic framework for analysing multi-hazard interactions and their impact on maternal and child health outcomes. By focusing on Zambia as a case study, this research aims to generate insights that are both contextually relevant and scalable for global application.&amp;#160; Keywords: Multiple hazards, maternal and child health, machine learning, copula models, Bayesian networks&amp;#160; &amp;#160;Acknowledgements&amp;#160; This work was conducted under the framework of the Economic and Social Research Council grant: Building Resilience to Floods and Heat in the Maternal and Child Health Systems in Brazil and Zambia (REACH), Grant Number: ES/Y00258X/1

Cow-calf operations form the foundation of the U.S. beef industry, with approximately 27.9 million beef cows and 25.1 million calves born in 2024, comprising 61.1% of total cattle inventory. Though their exact economic contribution is hard to predict due to the integrated nature of the beef supply chain, the cattle industry contributed approximately $88.4 billion (17%) to the total U.S. agricultural receipts in 2023. Despite their significance, these operations face economic challenges, including fluctuating feed costs, market volatility, and drought. Given the narrow profit margins of these operations, predictive modeling of profitability can offer valuable insights for sustainable management and decision-making. This study applies three univariate (Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with Exogenous regressors (SARIMAX)), three multivariate machine learning models (Random Forest (RF), AdaBoost, Support Vector Regression (SVR)), and one deep learning model (Long Short-Term Memory, LSTM) to assess the economic performance of cow-calf operations, specifically predicting the average farm price received for calves ($/Cwt). The analysis leverages historical data along with key domain-specific variables, including feed costs (monthly average U.S. farm price of corn, $/bushel), fuel prices (industrial natural gas price, $/thousand cubic feet), foreign exchange rates (U.S.-Japan exchange rate), and inflation indices (Consumer Price Index, CPI), to provide robust and enhanced predictive insights. A cow-calf budget dataset spanning from 1996 to 2024 was compiled from sources including the USDA Agricultural Marketing Service and Texas A&amp;M AgriLife Extension. Data acquisition was facilitated through web scraping techniques, enabling the automated extraction of the historical economic indicators. The collected data underwent preprocessing and feature engineering, to enhance model input quality, and both univariate time-series and multivariate machine learning models were applied to capture temporal and nonlinear patterns in calf prices. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Mean Percentage Error (MPE), Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), and Coefficient of Determination (R²) to comprehensively assess predictive performance. The analysis revealed substantial relationships between selling price of cow-calf operations and key economic indicators. Among the evaluated forecasting models, SARIMA exhibited the strongest performance by effectively capturing seasonal trends, while SARIMAX showed comparable accuracy through the integration of external variables. Among the machine learning techniques, AdaBoost performed best, outperforming RF and SVR. The LSTM model achieved the overall best performance, demonstrating strong capability in modeling both nonlinear dependencies and temporal dynamics in calf price trends. These findings demonstrate the potential of combining classical time-series and modern machine learning models to support price forecasting in cow-calf operations. This hybrid modeling approach provides a powerful decision-support tool for producers navigating market volatility, and future research should explore real-time forecasting and broader economic and environmental integrations to further strengthen predictive capabilities.

It is important to incorporate diverse dependence structures between stocks when managing a stock portfolio. Copulas are a useful statistical tool to capture dependence structure, dealing with both the linear and non-linear association that may occur in the tails of data. Financial time series datasets often exhibit volatility clustering that affects price forecasting accuracy. This work proposes the initial use of the principal component analysis followed by a copula and GARCH model that filters the effect of the volatility clustering in the series. For illustration, we consider ten banks from which Bank of America and PNC Financial Services Group are chosen, and then we project their future price movements through simulations. Since they are selected in terms of the principal component analysis, the procedures could help the proposed model to become a more widely used tool in forecasting financial stock performance.