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

Purpose This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects. Design/methodology/approach The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets. Findings The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements. Practical implications The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss. Originality/value The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

Copula models have become very popular and well studied among the scientific community.[...]

Dynamic linkages and the propagation mechanism driving major international stock markets: An analysis of the pre- and post-crash eras

This research explores the copula-based linkages between oil and gold prices, exchange rates, and stock markets in five Asian economies: Pakistan, India, Indonesia, Malaysia, and China. Using data from 2017 to 2024 and advanced copula models, we capture the non-linear and tail dependencies among these variables, particularly during periods of financial distress (COVID19). The findings indicate varying degrees of correlation across these economies, with oil price fluctuations significantly affecting stock returns in oil-exporting nations such as Indonesia and Malaysia, while gold prices serve as a stabilizing asset in India and Pakistan. Exchange rate volatility further influences stock market performance, particularly in China. The copula models reflected the non-linear and asymmetric dependencies especially during the extreme scenarios of the market while giving the investors understanding of risk management and portfolio diversification. These findings contribute to a deeper understanding of the interconnectedness of commodity prices, exchange rates, and stock markets in Asian economies, with practical implications for policymakers and investors. It can help investors and financial analysts to gain a better understanding on the interdependence of such variables and can promote their investment decision making process.

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis.

Purpose – The purpose of this chapter is to present an investigation on the dynamic linkages between global macro hedge funds and traditional financial assets of developed and emerging markets. Methodology/approach – To explore relationships among these price indices, we analyse Granger causality and vector autoregression (VAR) dynamics through impulse response functions. Besides, multivariate cointegration is used to know long-term relationships between assets and allows risk-averse investors to reduce uncertainty. Finally, a vector error correction model (VECM) provides active asset managers the opportunity to anticipate short-term price movements. Findings – Our results show that in a Granger causality sense, we observe long- and short-term relationships between global macro hedge funds and traditional financial assets for Canada, France and Germany. This implies that opportunities for international portfolio diversification are significantly lower for countries having relationships between assets. For Canada, France and Germany, the risk-averse investors can reduce their long-term volatility by investing according to the cointegrating vector, whereas active managers can benefit from the knowledge of short-term asset price movements. The VEC Pairwise Granger causality in the short term confirms our analysis of causality according to VAR models. Originality/value of paper – These results are original because they help the investor to understand the dynamics of the relationship between global macro hedge funds and traditional financial assets.

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.

Currently, there is significant political and economic dependence on the palm oil industry in Malaysia, Indonesia, and Thailand. They are the most significant world producers and exporters. The objective of this study is to investigate the factors affecting the sustainable improvement of world production, consumption, and price in the palm oil market. This study employed a vector error correction model (VECM) along with an ex-post forecast approach. The data used monthly data from January 2014 to December 2019, covering 72 observations of pre-COVID-19 periods for analysis purposes. The study found that there were significant long-term relationships among the variables representing palm oil consumption, world population, and soybean oil price for palm oil price. Additionally, there were short-term relationships among exchange rates, palm oil production, and consumption for price. Changes in world total palm oil production are based solely on changes in world palm oil price. Moreover, changes in world total palm oil consumption are also based on world population, soybean oil price, and changes in world palm oil price. In summary, companies and governments that proactively implement sustainable measures will be better positioned to navigate regulatory challenges, maintain market access, and ensure long-term industry stability.

Gold plays a central role in Thailand's investment landscape as a hedge against inflation and economic uncertainty; however, accurate price forecasting remains challenging due to high volatility and complex interactions among macroeconomic variables. This study proposes an integrated forecasting framework for Thai gold prices that combines Pearson correlation-based feature selection with the Vector Error Correction Model (VECM). Using daily data spanning 2019-2023, correlation analysis is first employed to identify key determinants with strong linear associations to domestic gold prices. The selected variables include global gold prices, total gold reserves, government debt, and the THB/USD exchange rate. Subsequently, a VECM is constructed to jointly model short-run dynamics and long-run equilibrium relationships among cointegrated variables. Model performance is evaluated using a rolling one-step-ahead forecasting scheme and benchmarked against three commonly used models: the Random Walk (naïve) model, the Autoregressive Integrated Moving Average (ARIMA) model, and the Vector Autoregression (VAR) model. Empirical results demonstrate that the proposed VECM consistently outperforms all benchmark approaches, achieving the highest explanatory power (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.9647$</tex>) alongside the lowest forecasting errors. These findings confirm the significance of both global and domestic macroeconomic transmission mechanisms in Thailand's gold market and highlight the effectiveness of cointegration-based modelling for reliable gold price forecasting.

This study investigates the impact of key macroeconomic variables on Sri Lanka's unemployment rate and compares various forecasting models, namely, econometric, Machine Learning (ML), and Deep Learning (DL) to improve unemployment rate prediction accuracy. Using quarterly data from 1998 to 2024, including GDP growth, inflation, interest rates, exports, exchange rates, and tourist earnings, the research evaluates relationships between these indicators and unemployment rate. Traditional econometric analysis, specifically the Vector Error Correction Model (VECM), is employed to capture long term relationships, while ML models (Random Forest, Support Vector Regression, and Extreme Gradient Boosting) and DL models (Feedforward Neural Network) address non-linear and complex patterns in data. Forecast evaluation shows that Random Forest provides the highest accuracy, with a Mean Absolute Error (MAE) of 0.13, outperforming other models. The VECM, while effective in capturing long term trends, has limitations in short-term forecasting due to linear assumptions. This study discusses the potential of ML and DL models in economic forecasting, offering robust supportive techniques to improve traditional econometric methods. These insights can support policymakers in proactive labor market interventions. Accordingly, the findings from this study highlight the need for a hybrid approach that combines economic theory, as well as the adaptability of blending non-conventional and conventional modeling techniques for economic analysis.

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.

his paper examines the dynamic linkage between gold price, exchange rates and stock market indices in an emerging market context, India. Consumption of gold is mostly common in household sector of India. Moreover, it can also be contemplated as an alternative investment route mainly to safeguard against financial risk obligations. The study considers 232 monthly observations of each of these variables from 1 st January 2000 to 30 th April 2019. Using Johansen Co-integration, we find a long run co-integration among gold price, exchange rate and stock market indices. The Vector Error Correction Model (VECM) shows the unilateral causality from stock market index and exchange rates to gold prices. Pairwise Granger causality exhibited bidirectional causality between exchange rate and gold prices. Our findings have important implications for financial market analysts, investors, regulators and policy makers in understanding the role of monthly stock price movement and exchange rates on gold prices in India.

In this paper we examine statistical relationships among European carbon markets from 2005 to 2010. We use a time-series approach using 1,220 daily (spot and forward) price data observations from Phase I and Phase II of the European Union Emissions Trading Scheme (EU ETS). Procedures such as unit root, cointegration, vector error correction models (VECMs), Granger causality, and generalised impulse response functions are employed in the analysis. The results reveal dynamic linkages among spot and forward carbon prices in Phases I and II, indicating that joint price discovery is taking place in carbon markets (at least in the short-run). However, evidence of constrained long-run information flows in Phase II, as indicated by the joint short- and long-run Granger causality testing, may be problematic for policy makers. This finding suggests that the coordinated policies designed to improve the operation and transparency of the EU ETS in Phase II may have actually been counter-effective. If carbon pricing mechanisms are dysfunctional, this has implications for the informational efficiency of carbon markets in Phase II and beyond, thus signalling the possibility of arbitrage and other profitable trading opportunities for market participants. Further research could attempt to address these issues.

A consensus among academics and policymakers holds that investing in human development not only improves lives, but also by itself promotes stellar economic growth. We investigate these claims by estimating the two‐way causality between economic growth and human development in Nigeria over the period from 1961 to 2015. By employing three statistical frameworks (Gregory–Hansen Cointegration, Stock–Watson Dynamic Ordinary Least Square and Vector Error Correction Model), our estimates suggest the following. First, economic growth and human development share a long run relationship, that is, they are cointegrated. Second, despite the two variables sharing a long run relationship, only economic growth can exercise a positive effect on human development, and no evidence of reverse causality was observable. Far importantly, we prescribe a policy recommendations from these findings. © 2018 John Wiley &amp; Sons, Ltd.

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.