Search Results (334)

Egypt has experienced a sharp rise in external debt over the past decade, increasing from USD 55.8 billion in 2015 to over USD 165.3 billion by 2023. Despite maintaining a debt-to-GDP ratio within internationally accepted thresholds (approximately 45% in 2023), the country faces mounting economic distress, including foreign exchange shortages, currency depreciation, and rising debt-servicing burdens. This study argues that Egypt’s crisis stems not from excessive borrowing but from ineffective debt management, particularly the misalignment between debt maturities and the economic returns of financed projects. Using annual data from 2010 to 2023—a period deliberately selected to capture Egypt’s post-2011 political and economic transition—the analysis applies a Vector Autoregression (VAR) model and Granger causality test to explore short-term interactions between short-term and long-term external debt, the exchange rate, and foreign reserves. While the small sample size limits long-term econometric inference, it provides meaningful insights into short-term debt dynamics and liquidity pressures characteristic of Egypt’s current economic phase. The results show that short-term debt exerts significant depreciative pressure on the currency, while long-term debt weakly undermines reserves when tied to non-revenue-generating projects. Policy recommendations emphasize improving debt maturity alignment, enhancing transparency, and linking debt servicing to productive investments. Full article

This paper investigates how global uncertainty and macro-financial shocks transmitted to BRICS+ equity markets between April 2016 and July 2025. A vector autoregressive (VAR) framework, complemented by Granger-causality tests, variance decompositions, and impulse response functions, is employed to examine four key drivers: U.S. financial market volatility (VIX), geopolitical risk (GPRD), U.S. inflation expectations (T5YIE), and the U.S. term spread (T10Y3M). The findings show that the VIX functions both as a recipient and a transmitter of shocks, amplifying volatility across BRICS+ markets, with India, Brazil, and the Gulf states acting as important nodes in the global contagion network. By contrast, geopolitical risk shocks have only short-lived effects on both U.S. yields and emerging equity markets. Shocks to U.S. inflation expectations and yield-curve dynamics transmit quickly to BRICS+ markets but dissipate within a few days, underscoring efficient market adjustment. Overall, the evidence points to a multipolar structure of global contagion in which BRICS+ markets exert growing influence alongside the United States. These results offer important implications for risk management, portfolio diversification, and policy coordination under heightened uncertainty. Full article

Localized employment gains from new or expanded fossil fuel development commonly are cited by its proponents in response to sustainability-related concerns raised by local drilling area residents. This paper analyzes local employment effects in drilling areas within the Marcellus shale formation in the state of Pennsylvania, USA. The Marcellus shale formation was one of the early natural gas fracking boom development areas globally, so these local employment outcomes can inform future policy decisions on not-yet-developed shale gas formations worldwide. As long-term sustainable jobs are a key part of any locale’s sustainable development program, the magnitude and persistence of employment gains in the local drilling area is highly relevant. The existing research literature on employment effects from increased shale gas extraction is dominated by usage of panel estimation on annual data at the U.S. state/county level. The innovative contribution of this paper is its use of monthly data, sub-state local areas (67 counties within PA), and a parsimonious vector autoregression model (VAR) estimated separately for each of the 67 counties. The estimated VAR models are used to ascertain whether Marcellus shale drilling activity in PA led to actual county-level employment above forecasted based on data prior to the shale boom. Actual versus forecasted employment is compared from 2010–2019. Higher than forecasted employment findings were much more likely to occur in approximately the top quarter of drilling counties, with the observed gains being modest. Most importantly, however, any employment gains above forecast were short-lived, gone within four years in most counties. Given the modest and temporary local employment gains found and the many known potential damages to local residents and the environment from intensive drilling, it is questionable that the local areas in the Marcellus shale formation most intensively drilled benefited overall from the shale gas extraction. These findings are germane to ongoing current debates about expanding natural-gas-fired electricity generation, versus solar plus storage, to meet anticipated large rises in electricity demand from rapid data center development globally. Full article

The urgency of transitioning to sustainable energy has accelerated amid climate change concerns and fossil fuel depletion. This study introduces a novel comparative framework that integrates Time-Varying Parameter Vector Autoregression (TVP-VAR) and Quantile Vector Autoregression (QVAR) models to examine both returns and realized volatility across renewable-energy tokens (Powerledger and Wepower), clean-energy stocks, and crude oil. This dual-method approach uniquely captures time-varying and tail-specific spillovers, extending previous studies that relied on a single model or ignored volatility interactions. Using daily data from February 2018 to January 2023, we reveal moderate but significant interconnectedness—about 30% on average—with stronger linkages during global crises such as COVID-19 and the Russia–Ukraine conflict. Renewable-energy tokens act mainly as net receivers of shocks, implying their role as protective diversification assets, while clean-energy stocks are net transmitters and oil alternates between both roles. These results highlight how digital assets interact with traditional energy markets under varying conditions. The study offers practical implications for portfolio diversification and emphasizes the need for transparent, supportive regulation to prevent tokens from amplifying systemic risk while promoting the stability of sustainable-energy investment markets. Full article

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability. Full article

This study investigates how cycling behaviour in urban and tourism areas of Slovenia responded to the COVID-19 pandemic and its aftermath, with implications for forecasting and sustainable mobility planning. Using high-frequency daily data from January 2020 to August 2024 across Ljubljana (urban) and Rateče (tourism), we model the interdependence between weather conditions, cycling volume, and reported COVID-19 cases. The results reveal contrasting dynamics: in Ljubljana, higher cycling activity correlates with fewer infections, supporting cycling as a low-risk commuting mode, whereas in Rateče, tourism-driven cycling coincides with higher variability in infections. Regression and vector autoregressive (VAR(2)) models highlight the significant roles of precipitation and sunlight in shaping these patterns and enable short-term forecasts of COVID-19 cases up to January 2025. Machine learning methods complemented the VAR model, improving forecasting accuracy and revealing nonlinear interactions. These findings demonstrate the value of integrating behavioural and environmental indicators into public health forecasting and support region-specific strategies for resilient, sustainable mobility during future health or climate disruptions. Full article

This study examines risk transmission between the United States and China using integrated economic policy uncertainty (EPU) and geopolitical risk (GPR) indices. We employ a dual methodology that combines Vector Autoregressive (VAR) and Granger causality in quantiles tests to analyze interactions during systemic leadership transitions, a dimension that is currently under-explored. Our dataset covers the period from June 2000 to June 2023. Results indicate that China is narrowing the economic influence gap and strengthening its role as a regional anchor. The U.S., however, maintains predominant global leadership. This dynamic reframes bilateral tensions as a “status dilemma” rather than a security conflict. Crucially, we identify asymmetric spillover effects: the U.S. uncertainty shocks spread globally, while China’s volatility remains regional. Our findings contribute to the understanding of financial stability by demonstrating that leadership asymmetries are critical determinants, providing valuable insights for designing systemic risk monitoring tools and contagion mitigation policies during periods of heightened uncertainty. Full article

The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI] and their applications to economic and financial time series. MAI has recently gained momentum because it can be seen as a link between two popular but distinct multivariate time series approaches: vector autoregressive modeling [VAR] and the dynamic factor model [DFM]. Indeed, on the one hand, MAI is a VAR model with a peculiar reduced-rank structure that can lead to a significant dimension reduction; on the other hand, it allows for the identification of common components and common shocks in a similar way as the DFM. Our focus is on recent developments of the MAI, which include extending the original model with individual autoregressive structures, stochastic volatility, time-varying parameters, high-dimensionality, and co-integration. In addition, some gaps in the literature are filled by providing new results on the representation theory underlying previous contributions, and a novel model is provided. Full article

Multiple time series forecasting is critical in domains such as energy management, economic analysis, web traffic prediction and air pollution monitoring to support effective resource planning. Traditional statistical learning methods, including Vector Autoregression (VAR) and Vector Autoregressive Integrated Moving Average (VARIMA), struggle with nonstationarity, temporal dependencies, inter-series correlations, and data anomalies such as trend shifts, seasonal variations, and missing data. Furthermore, their effectiveness in multi-step ahead forecasting is often limited. This article presents an Automated Machine Learning (AutoML) framework that provides an end-to-end solution for researchers who lack in-depth knowledge of time series forecasting or advanced programming skills. This framework utilizes Gated Recurrent Unit (GRU) networks, a variant of Recurrent Neural Networks (RNNs), to tackle multiple correlated time series forecasting problems, even in the presence of anomalies. To reduce complexity and facilitate the AutoML process, many model parameters are pre-specified, thereby requiring minimal tuning. This design enables efficient and accurate multi-step forecasting while addressing issues including missing values and structural shifts. We also examine the advantages and limitations of GRU-based RNNs within the AutoML system for multivariate time series forecasting. Model performance is evaluated using multiple accuracy metrics across various forecast horizons. The empirical results confirm our proposed approach’s ability to capture inter-series dependencies and handle anomalies in long-range forecasts. Full article

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

In this paper, based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, five financial serials are dynamically weighted, and then China’s Financial Conditions Index is synthesized to measure China’s financial cycle. After that, using the monthly data of 2000–2023 as sample space, this paper utilizes the Markov Switching (MS) model to analyze the characteristics of China’s financial cycle and to investigate the four-zone system. Then, the Vector Autoregression (VAR) model focuses on investigating the macroeconomic effects of China’s financial cycle. The findings are as follows: Firstly, the dynamic weighting approach based on GARCH model is more suitable for valuating China’s financial cycle. Secondly, China’s financial cycle has a strong inertia at the state of transition and the imbalance of China’s overall financial situation is very common. Additionally, China’s financial cycle is distinctly characterized by the double asymmetry of fewer contractions and more expansions, shorter expansions, and longer expansions. Thirdly, China’s financial expansion offers a nine-month short-term stimulus to output and exerts lasting upward pressure on prices. Full article

This study is designed to investigate the dynamic risk transmission processes between clean energy ETFs and ESG indices in the BRICS countries—Brazil, India, China, and South Africa—while excluding Russia due to the lack of consistent data availability during the study period, which coincides with the Russia–Ukraine conflict. The analysis is conducted on daily data obtained from DataStream, spanning from 27 October 2021 to 5 January 2024. By applying a time-varying parameter vector autoregression (TVP-VAR) modeling framework, we considered examining the global market conditions and economic shocks’ effects on these indices’ interconnectedness, including COVID-19 and geopolitical tensions. In this context, clean energy ETFs turned out to stand as net shock transmitters throughout volatile market spans, while ESG indices proved to act as net receivers. Moreover, we undertook to estimate both of the minimum variance and minimum connectedness portfolios’ hedging efficiency and performance. The findings highlight that introducing clean energy indices into investment strategies helps boost financial outcomes while maintaining sustainability goals. Indeed, the minimum connectedness portfolio consistently delivers superior risk-adjusted returns across varying market circumstances. In this respect, the present study provides investors, regulators, and policymakers with practical insights. Investors may optimize their portfolios by integrating clean energy and ESG indexes, useful for achieving financial and sustainability aims. Similarly, regulators might apply the findings to establish reliable green investment norms and strategies. Thus, this work underscores the crucial role of dynamic portfolio management in optimizing risk and return in the globally evolving green economy. Full article

Understanding the nature of monetary and fiscal policy interactions has gained more importance over the years, especially within the context of the global financial crisis and the recent COVID-19 pandemic. This study uses a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model and a Markov Switching Dynamic Regression (MSDR) framework to explore the dynamics of monetary–fiscal policy interactions in South Africa. The analysis employs time series data from 1994 to 2023 and tests the dynamic response of key macroeconomic variables to positive monetary and fiscal policy shocks. Furthermore, the MSDR framework is utilised to analyse how policy behaviour evolves during regime change. The TVP-VAR results show that fiscal expansions led to a positive response in GDP over time, a stable interest rate reaction post-COVID-19, and a consistently negative CPI response, contradicting conventional theory. The MSDR analysis reveals a dominant regime where monetary policy is active and fiscal policy is passive, with a positive interaction between interest rates and government spending, likely reflecting South Africa’s high debt environment. These findings underscore the importance of understanding policy interactions’ landscape to inform policy decisions better and minimise sub-optimal policy outcomes. Full article

Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to simultaneously quantify these characteristics using a conventional single (linear or nonlinear) model may lead to inaccurate and costly results. To address this, we propose a hybrid RVM-WT-AdaBoostRT-RF framework using power grid data from the Electricity Supply Commission (Eskom) of South Africa. To achieve model interpretability, the least absolute shrinkage and selection operator (LASSO) is first applied to remedy the adverse effects of multicollinearity through regularisation and variable selection. Secondly, a random forest (RF) is used to select the top 10 most influential variables for each season for further analysis. A relevance vector machine (RVM) captures complex nonlinear relationships separately for each season, while the wavelet transform (WT) decomposes residuals generated from RVM into different frequency subseries (with reduced noise). These subseries are predicted with minimal bias using AdaBoost with regression and threshold (AdaBoostRT). Finally, we stack RVM, AdaBoostRT, RF, and residual individual predictions using RF as a meta-model to produce the final forecast with minimal error accumulation and efficiency. The comparative study, based on point forecast metrics, the Diebold-Mariano test, and prediction interval widths, shows that the proposed model outperforms vector autoregressive (VAR), RF, AdaBoostRT, RVM, and Naïve models. The study results can be utilised for optimising resource allocation, effective power grid management, and customer alerts. Full article

This study explores the new insights into the integration and dynamic asymmetric volatility risk spillovers between Bitcoin, currency pairs (USD/ZAR, GBP/ZAR and EUR/ZAR), and traditional financial assets (ALSI, Bond, and Gold) in South Africa using daily data spanning the period from 2010 to 2024 and employing Time-Varying Parameter Vector Autoregression (TVP-VAR) and wavelet coherence. The findings revealed strengthened integration between traditional financial assets and currency pairs, as well as weak integration with BTC/ZAR. Furthermore, BTC/ZAR and traditional financial assets were receivers of shocks, while the currency pairs were transmitters of spillovers. Gold emerged as an attractive investment during periods of inflation or currency devaluation. However, the assets have a total connectedness index of 28.37%, offering a reduced systemic risk. Distinct patterns were observed in the short, medium, and long term in time scales and frequency. There is a diversification benefit and potential hedging strategies due to gold’s negative influence on BTC/ZAR. Bitcoin’s high volatility and lack of regulatory oversight continue to be deterrents for institutional investors. This study lays a solid foundation for understanding the financial dynamics in South Africa, offering valuable insights for investors and policymakers interested in the intricate linkages between BTC/ZAR, currency pairs, and traditional financial assets, allowing for more targeted policy measures. Full article