Search Results (129)

Accurate volatility forecasting and timely risk early warning are foundational requirements of financial risk management: Value-at-Risk estimates, portfolio risk limits, derivative hedging ratios, and stress-test scenario calibrations all depend on forward-looking volatility signals that remain reliable when market conditions depart from average. This paper develops a public-data causal multiscale wavelet spillover learning (CMWSL) framework that jointly addresses stock-index volatility forecasting and high-volatility early warning under strict walk-forward evaluation. CMWSL integrates three components: a heterogeneous autoregressive (HAR) persistence block as the dominant linear baseline, causal stationary wavelet transform (SWT) summaries that encode within-index multiscale market dynamics, and a cross-index spillover layer that tests whether medium- and long-scale wavelet energy from peer indices carries incremental risk-relevant information. The empirical analysis covers the S&P 500, Nasdaq-100, and Dow Jones Industrial Average over a 2513-step out-of-sample evaluation period from 2016 to 2025, with forecast horizons $h\in \{1,5,10\}$ and OHLC-based volatility targets. All preprocessing, wavelet decomposition, calibration rules, and warning thresholds are re-estimated inside each rolling training window to eliminate look-ahead bias. HAR remains the strongest average model in the main Rogers–Satchell specification, confirming that daily index volatility risk is highly persistence-driven. The multiscale extension delivers statistically significant improvements at longer horizons, in richer public macro-financial information environments, and under the Parkinson target. Clark–West tests detect significant spillover gains in five of nine index–horizon cells (CW $=4.83$, $p<0.001$ for S&P 500 at $h=10$). Critically, tail-conditioned and rolling-window diagnostics show that multiscale and cross-index gains concentrate in upper-volatility regimes and synchronized stress episodes—precisely the conditions in which risk management decisions are most consequential. For market-risk early warning, a logistic classifier built on the same causal feature pipeline delivers the most stable precision–recall performance across all settings, providing an interpretable and operationally auditable alert mechanism suitable for practical risk monitoring. Full article

Hydropower-dominated electricity systems are increasingly exposed to hydroclimatic variability, making anticipatory streamflow information essential for energy security, operational resilience, and sustainable planning. This study develops a transparent monthly early-warning framework for the Paute River basin, Ecuador, a strategically important hydrological system for national hydropower generation. Using a 42-year series of observed and compiled monthly streamflow records from 1984 to 2025 (n = 504), the framework derives seasonal low-flow thresholds (P20 warning and P10 critical) and fits a Seasonal Autoregressive Integrated Moving Average model to log-transformed flows. The resulting lognormal predictive distribution provides point forecasts, prediction intervals, and probabilities of low-flow events. Predictive skill was assessed through a 2016–2025 rolling-origin validation with 120 one-step-ahead forecasts and benchmarks against Error–Trend–Seasonal Holt–Winters and seasonal naive models. The SARIMA-log specification achieved the best point accuracy (MAE = 38.80 m³/s, RMSE = 47.62 m³/s, sMAPE = 32.63%) and modest but useful probabilistic skill (CRPSS = 0.069; Brier Skill Score = 0.169 for Q < P20 and 0.274 for Q < P10). A threshold-sensitivity analysis showed that the 0.15 and 0.30 alert thresholds represent a deliberate trade-off between early detection and false-alarm reduction. For 2026, August displayed the highest low-flow probability (P(Q < P20) = 0.303), triggering a moderate Hydropower Low-Flow Risk Traffic-Light category. The contribution is not a new forecasting algorithm but an operationally auditable integration of seasonal thresholds, probabilistic forecasting, verification, and risk communication for hydropower energy-security governance in the tropical Andes. Full article

This paper investigates the role of financial institution development in promoting renewable energy generation in African economies. The paper is motivated by the increasing global emphasis on clean energy transition and the need to achieve the Sustainable Development Goals, particularly those related to affordable and clean energy and climate action. It focuses on identifying the mechanisms through which financial development influences renewable energy outcomes. Grounded in the Schumpeterian theory of finance, the paper argues that financial institutions facilitate innovation and structural transformation by allocating resources toward productive investments, including renewable energy projects. The analysis examines whether credit to the private sector serves as a mediating channel in this relationship. It also evaluates the moderating roles of institutional quality and natural resource rents. Using a Panel Autoregressive Distributed Lag (PARDL) model within a dynamic fixed-effects error correction framework, the findings reveal a nonlinear relationship. Financial institution development initially promotes renewable energy generation, but its positive effect weakens beyond a threshold of resource dependence. Institutional quality strengthens the effectiveness of financial development, while credit to the private sector fully transmits its impact on renewable energy generation. The results highlight the importance of strengthening financial systems, improving governance, and enhancing private sector credit allocation to support sustainable energy development in Africa. Full article

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric. Full article

A strong transportation infrastructure is critical in advancing ICT trade by facilitating the efficient movement of goods and services. This efficiency enhances supply chains and attracts greater foreign direct investment, ultimately supporting technological development and boosting the economy. This article evaluates the relationship between transportation infrastructure (TI), information and communication technology trade openness (ICT trade), foreign direct investment (FDI), and economic growth (GDP) in Saudi Arabia from 1990 to 2023. Using the Autoregressive Distributed Lag (ARDL) model, we found that ICT trade has a statistically significant positive effect on long-run GDP growth. However, in the short run, ICT trade has a positive but non-significant impact on GDP growth. Additionally, the results show that TI has a statistically significant negative effect on short-run GDP growth. Moreover, the non-linear Threshold Regression model results show a threshold value for information and communication technology trade openness (ICT trade) of approximately 0.4051. Specifically, the findings indicate that increased ICT trade reduces the negative impact on economic growth beyond a certain threshold. This study is highly significant for Saudi Arabian decision-makers, as it highlights the roles of transportation infrastructure and ICT trade in attracting FDI and bolstering the economy. Full article

This study applies a multiple threshold nonlinear autoregressive distributed lag (MTNARDL) model to examine the asymmetric impact of real exchange rate volatility on Vietnam’s exports and imports with its three leading trading partners: China, the United States, and South Korea. By allowing trade responses to vary across different volatility regimes, the MTNARDL framework provides a flexible approach to capturing potential nonlinear adjustment dynamics that cannot be addressed by single-threshold models. Moreover, using bilateral import and export data helps reduce aggregation bias. The results indicate the presence of asymmetric long-run adjustment dynamics in the relationship between real exchange rate volatility and bilateral trade flows, while short-run effects are generally weak and less consistent across trading partners. These findings provide valuable insights into the complex effects of exchange rate volatility, enabling policymakers to more effectively design and manage policies to mitigate its impact. Full article

City noise is a significant health issue, particularly in expanding metropolitan areas like Charlotte, North Carolina. This research examines the potential for vegetation density to mitigate transportation noise as a sustainable solution. The analysis used the Normalized Difference Vegetation Index (NDVI), population density, and transportation noise zones from the USA Department of Transportation, evaluated at the census block group level all for 2020. To identify overall patterns, a Spatial Autoregressive (SAR) model and Geographically Weighted Regression (GWR) were used for spatial structure and local variation, with distance to high-noise transportation zones used as a proxy for extreme noise exposure. The SAR model (AIC = 8476.5; RMSE = 1174.7 m) revealed a vegetation threshold of 35.2%, beyond which the benefits of vegetation for noise buffering became more pronounced. The GWR model uncovered spatial heterogeneity in the strength of vegetation’s effect, with stronger mitigation in southern and eastern parts of Charlotte. To conclude, we propose a three-tier spatial framework to prioritize neighborhoods for green infrastructure investment, particularly those with low vegetation, higher population density, and low natural noise protection. These findings emphasize the importance of incorporating vegetation density thresholds and spatial variability into noise mitigation strategies to support sustainable urban environments. Full article

This study examines the determinants of inflation volatility in Tunisia, focusing on central bank independence (CBI), economic transparency, and macroeconomic fundamentals. Although CBI is widely regarded as essential for monetary credibility, its effectiveness depends on its institutional framework. Our contribution is twofold. First, we develop a theoretical framework based on game theory to illustrate how the effectiveness of economic transparency and CBI shapes the welfare of both the central bank and the private sector in the presence (or not) of fiscal policy. Second, we use a binary threshold nonlinear autoregressive distributed lag (NARDL) model to capture long-run relationships and a Markov-switching GARCH (MS-GARCH) framework to model volatility dynamics. As a continuous measure, CBI has no significant impact on volatility. Paradoxically, high de jure independence in a binary regime is associated with a slight increase in inflation fluctuations. This indicates that legal independence alone is insufficient without fiscal discipline or effective coordination between the monetary and fiscal authorities. Notably, under fiscal pressure, greater CBI substantially reduces inflation volatility, highlighting the need for a coherent macroeconomic framework. Economic transparency generally increases short-term volatility but stabilizes inflation when supported by credible fiscal signals. Among the macroeconomic fundamentals, volatility in broad money is strongly destabilizing, whereas fluctuations in industrial production and the real exchange rate are largely insignificant. Government spending and exposure to external shocks, including import prices and geopolitical risks, further amplify this volatility. The observed negative trend over time reflects gradual improvements owing to policy reforms. Policy recommendations emphasize the establishment of genuinely independent and credible monetary institutions, enhancing coordination with fiscal policy, improving communication strategies, and strengthening risk management. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

This research explores the application of extreme value theory in modelling and quantifying tail risks across different economic equity markets, with focus on the Nairobi Securities Exchange (NSE20), the South African Equity Market (FTSE/JSE Top40) and the US Equity Index (S&P500). The study aims to recommend the most suitable probability distribution between the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) and to assess the associated tail risk using the value-at-risk and expected shortfall. To address volatility clustering, four generalised autoregressive conditional heteroscedasticity (GARCH) models (standard GARCH, exponential GARCH, threshold-GARCH and APARCH (asymmetric power ARCH)) are first applied to returns before implementing the peaks-over-threshold and block maxima methods on standardised residuals. For each equity index, the probability models were ranked based on goodness-of-fit and accuracy using a combination of graphical and numerical methods as well as the comparison of empirical and theoretical risk measures. Beyond its technical contributions, this study has broader implications for building sustainable and resilient financial systems. The results indicate that, for the GEVD, the maxima and minima returns of block size 21 yield the best fit for all indices. For GPD, Hill’s plot is the preferred threshold selection method across all indices due to higher exceedances. A final comparison between GEVD and GPD is conducted to estimate tail risk for each index, and it is observed that GPD consistently outperforms GEVD regardless of market classification. Full article

Accurate forecasting of China’s Consumer Price Index (CPI) is crucial for effective macroeconomic policymaking, yet remains challenging due to structural breaks and nonlinear dynamics inherent in the inflation process. Traditional linear models, such as ARIMA, often fail to capture threshold effects and regime shifts. This study introduces a Threshold Autoregressive Moving Average (TARMA) model that embeds a nonlinear threshold mechanism within the conventional ARMA framework, enabling it to better capture the CPI’s complex behavior. Leveraging an evolutionary modeling approach, the TARMA model effectively identifies high- and low-inflation regimes, offering enhanced flexibility and interpretability. Empirical results demonstrate that TARMA significantly outperforms standard models. Specifically, regarding the CPI Index level, the out-of-sample Mean Absolute Percentage Error (MAPE) is reduced to approximately 0.35% (under the S-BIC integration scheme), significantly improving upon the baseline ARIMA model. The model adapts well to inflation regime shifts and delivers substantial improvements near turning points. Furthermore, integrating an information-criterion-based weighting scheme further refines forecasts and reduces errors. By addressing the limitations of linear models through threshold-driven nonlinearity, this study offers a more accurate and interpretable framework for forecasting China’s CPI inflation. Full article

Currently, factors such as geopolitical conflicts, frequent extreme weather events, and power struggles among major countries are threatening the stability of the global supply chain. Building a more resilient supply chain has received international consensus. Today, new quality productivity (NQP), spawned by disruptive innovation, is an important way for China to enhance its agricultural product supply chain resilience (SCR). However, studies often overlook the “time lag” problem of the panel data adopted, and their empowering paths require further investigation. Therefore, this study firstly constructs NQP and agricultural product SCR indicators. Based on the panel data produced by 31 Chinese provinces from 2011 to 2022, we solved the “time lag” problem by integrating a Backpropagation Neural Network (BPNN) with an Autoregressive Integrated Moving Average (ARIMA) model to predict the NQP level. Subsequently, the empowering paths through NQP-enhancing agricultural product SCR were explored via entropy weight TOPSIS and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method. Foundations: China’s agricultural product SCR exhibits a spatial differentiation characteristic of “prominent in the central region and weak in the western region”. A single factor is not a necessary condition for high resilience, and its improvement depends on the synergy of multiple factors. Three differentiated driving paths have been identified: “autonomous endogenous driving type”, “environment-enabled driving type”, and “system architecture driving type”. NQMP has become the bottleneck for improving agricultural product SCR, and the threshold of each factor has increased significantly as the resilience target is raised. High resilience stems from the synergy and functional compensation of core factors, while low resilience is mostly caused by the concurrent absence of key conditions or structural mismatch, showing distinct “multiple concurrencies” and “causal asymmetry” characteristics. Full article

Ozone density at cold-point mesopause (O3-CPM) can provide information on long-term atmospheric trends. Compared to ground-level ozone, O3-CPM is not only adversely affected by chemical substances emitted from human activities but is also regulated by solar radiation. Therefore, an accurate prediction of O3-CPM is necessary. However, it is difficult for traditional forecasting methods to predict the main trends and seasonal characteristics of ozone time series while capturing the random components and noise of O3-CPM. In order to improve the prediction accuracy of O3-CPM, this paper proposes a hybrid SSA-SARIMA-GSVR model based on the Singular Spectrum Analysis (SSA) method, which combines the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) and the Gray Wolf Algorithm Optimized Support Vector Regression Algorithm (GSVR). First, the O3-CPM sequence is decomposed using SSA, and the concept of reconstruction threshold (RT) is introduced to categorize the decomposed singular values into two classes. The categorized RT reconstructed sequences containing periodic features and major trends are fed into the SARIMA model for prediction, and the N-RT reconstructed sequences (original sequence N minus RT reconstructed sequence) containing stochastic components and nonlinear features are fed into the GSVR model for prediction. The final prediction results are obtained by superimposing the outputs of these two models. The results confirm that, compared to various commonly used time series forecasting models such as Long Short-Term Memory (LSTM), Informer, SVR, SARIMA, GSVR, SSA-GSVR, and SSA-SARIMA models, the proposed SSA-SARIMA-GSVR hybrid prediction model has the lowest error evaluation metrics, enabling accurate and efficient prediction of the O3-CPM time series. Specifically, the proposed model achieved an RMSE of 0.26, MAE of 0.212, and R² of 0.987 on the test set, outperforming the best baseline model (SARIMA) by 45.8%, 42.1%, and 3.1%, respectively. Full article

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 study re-examines the ongoing debate over corporate disclosure frequency amid renewed calls to replace quarterly with semiannual reporting in U.S. markets. While traditional theories hold that frequent disclosure enhances informational efficiency by reducing asymmetry, emerging evidence highlights trade-offs involving managerial myopia, earnings management, and heightened short-term volatility. Using data from 2007 to 2024, the study compares Dow Jones Industrial Average firms, which report quarterly, with STOXX 50 firms, which report semiannually, to assess how disclosure cadence affects market reactions to earnings news The methodology involves identifying volatility regimes using Self-Exciting Threshold Autoregressive (SETAR) models, estimating dynamic betas with the GARCH(1,1) model, and analyzing shock transmission through vector autoregressions with cumulative impulse response functions (CIRFs). The results show that quarterly reporters exhibit larger immediate price reactions but faster normalization, implying that more frequent reporting accelerates information assimilation while amplifying contemporaneous volatility. Sectoral heterogeneity is pronounced: cyclical industries display higher beta volatility and steeper, but shorter-lived responses, whereas defensive stocks exhibit smoother convergence. These findings suggest that disclosure frequency influences both the intensity and duration of information shocks, providing insights for regulators who aim to balance transparency, market efficiency, and reporting costs across varying volatility and sectoral environments. Full article