Search Results (184)

This paper examines how international crude-oil price movements are transmitted to natural-rubber prices through the petrochemical–synthetic-rubber chain, with implications for Thailand as the world’s leading natural-rubber exporter and China as the dominant consumer. Using monthly data from April 2003 to March 2026 on the OPEC reference basket, butadiene, styrene–butadiene rubber (SBR), and the Shanghai natural-rubber benchmark, the analysis combines a nonlinear ARDL specification with a Pesaran–Shin–Smith bounds test, a long-run association decomposition into direct and synthetic-rubber-mediated components with bootstrap inference, and a threshold-NARDL extension that conditions the decomposition on the inventory state. Three findings stand out. First, the synthetic-rubber-mediated component accounts for approximately three-quarters of the estimated oil–natural rubber long-run association (73.5 percent, 95 percent bootstrap CI [60.6, 87.2]), with the residual direct component accounting for the remainder. Second, long-run pass-through is directionally consistent with concentration in the synthetic-rubber component, although Wald tests do not reject symmetry at conventional levels for either the synthetic-rubber component (Wald $p=0.135$) or the direct oil component ($p=0.166$). Third, the synthetic-rubber-mediated share is consistently larger in low-inventory regimes by 26 to 66 percentage points across three alternative regime variables, although the magnitude amplification of asymmetric pass-through itself is not robust. Asymmetric local projections and a Diebold–Yilmaz spillover analysis are reported as complementary horizon-indexed and network checks. The results imply that the synthetic–natural rubber spread, conditioned on the inventory state, may be more informative for natural-rubber price-risk monitoring than crude-oil prices alone. These findings have implications for commodity price-risk monitoring, export-income exposure, and stabilisation design in rubber-exporting economies. Because crude-oil shocks are not externally identified, all estimates are interpreted as decompositions of long-run association rather than causal mediation effects. Full article

This study examines the dynamic relationships among oil prices, monetary conditions, and nominal GDP growth in Saudi Arabia, with particular attention to short-run adjustment and long-run equilibrium patterns in an oil-dependent economy operating under a fixed exchange-rate regime. Rather than identifying structural monetary policy shocks, the study focuses on reduced-form dynamic associations between market-based monetary indicators, oil-price movements, and nominal economic activity. Using a high-frequency monthly dataset covering key macroeconomic variables, the analysis employs the Autoregressive Distributed Lag (ARDL) framework to estimate both short-run dynamics and long-run equilibrium relationships. An Error Correction Model (ECM) is used to capture the speed of adjustment toward equilibrium, while Granger causality tests assess short-term predictive linkages. The empirical results reveal that monetary indicators, particularly interest rates and money supply, exhibit lagged and non-monotonic associations with nominal GDP growth, reflecting delayed transmission under exchange-rate constraints. Oil-price movements emerge as a dominant driver, showing strong contemporaneous and lagged associations with growth, whereas inflation and exchange-rate movements display limited short-run predictive relevance. The ECM results indicate relatively rapid convergence toward long-run equilibrium, suggesting efficient adjustment dynamics. Granger causality findings further confirm the short-term predictive content of key macroeconomic variables. By integrating high-frequency data with ARDL–ECM estimation, VAR-based robustness checks, and sensitivity analysis, the study provides evidence on how oil-price movements, liquidity conditions, and interest-rate dynamics jointly shape growth fluctuations in Saudi Arabia. Full article

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index $\overline{\alpha }=1.667$ across all constituents, substantially below the Gaussian benchmark of $\alpha =2.0$; (ii) Hurst exponents clustering below 0.5 ($\overline{H}=0.417$), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes. Full article

Road infrastructure projects are frequently affected by cost overruns driven by volatility in critical construction inputs and by the uneven association between external market shocks and material price movements. However, existing studies still provide limited evidence on how comparative forecasting, temporal price-signal diagnostics and probabilistic simulation can be integrated into a contingency-oriented decision framework. This study examines how construction input price forecasting and probabilistic simulation can inform contingency estimation in a road infrastructure case study. The empirical application is based on a Peruvian bridge project and combines benchmark-oriented forecasting using Bi-GRU and Random Walk models, descriptive temporal diagnostics based on lead–lag assessment and rolling-correlation analysis, and Monte Carlo simulation. Monthly series for structural steel, construction steel, cement, and diesel were transformed into log-returns and evaluated under a strict chronological design, while oil, the exchange rate, and the consumer price index were incorporated as exogenous variables. The Random Walk model produced lower forecasting errors for most inputs, achieving lower RMSE values in seven of the eight input-period comparisons; Bi-GRU outperformed it only for diesel in the test subset, with a 7.24% lower RMSE. From a project cost-risk perspective, the P95 contingency was estimated at 3.92% under Bi-GRU and 3.96% under Random Walk, indicating a similar upper-percentile contingency envelope under both forecasting specifications. The findings support contingency as a confidence-based budgeting decision rather than a fixed percentage. Full article

Research Purpose: This study investigates the role of digital investor behavior, measured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Daily data from 2020 to 2025 were collected, covering both crisis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frameworks, with Max Drawdown employed as a complementary risk metric to capture extreme market movements. Findings: Digital investor attention shows a predictive association with volatility, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predictive performance. The EGARCH model successfully captured the asymmetric impact of negative shocks (γ < 0, p < 0.05), while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress, achieving an R² of 95.36%. Scientific value: This study positions digital attention as a complementary variable that improves forecasting, moving beyond conventional price-based models in volatility modeling; by integrating Google Trends with technical analysis, the research introduces a hybrid forecasting framework that can be adapted to other emerging markets. Practical Implications: The findings offer practical value for policymakers and investors. Regulators can use digital attention measures as early warning signals to anticipate volatility, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools. Full article

This paper investigates the dynamic linkages and volatility transmission among global food prices, biofuel commodity prices, and crude oil prices, with a focus on the profound disruptions caused by the COVID-19 pandemic. While interdependencies between energy and agricultural markets are well-studied, the specific role of biofuels as a transmission channel and the exacerbating effects of the crisis remain underexplored, especially through a robust multivariate volatility framework. Utilizing A VECM-CCC-GARCH models, this study captures both mean and conditional variance dynamics, allowing for the examination of asymmetric news impacts and volatility spillovers. The analysis employs a comprehensive dataset including the FAO Food Price Index, key biofuel, ethanol, biodiesel, and crude oil prices (Brent and WTI), alongside proxies for the pandemic’s severity. The research hypothesizes that the COVID-19 pandemic significantly amplified the volatility and strengthened the price transmission channels. We expect to find increased co-movement and volatility spillovers, reflecting reduced demand for transport fuels, agricultural supply chain disruptions, and shifting biofuel production incentives. The TARCH component will discern if negative news (e.g., sharp drops in oil demand) had a disproportionately larger impact on volatility than positive news. By providing a nuanced understanding of these complex interdependencies, this study offers valuable insights for policymakers addressing food security, energy transition strategies, and macroeconomic stability in the post-pandemic world, particularly concerning the strategic role of biofuels. Full article

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses. Full article

Coffee-price volatility is a recurrent external shock for Peru’s small open economy, with potentially uneven consequences across sectors. This study evaluates whether global coffee prices and domestic macro-agricultural indicators share stable long-run equilibria and quantifies the transmission of coffee-price shocks to the terms of trade, nominal exchange rate, consumer prices, agricultural GDP, and total GDP. Using a multivariate vector error-correction model identified via Johansen cointegration, and controlling for major global disruptions and ENSO-related seasonality, we trace dynamic effects through impulse-response analysis. The results indicate economically meaningful cointegration, implying that external prices and domestic aggregates are linked by long-run restrictions. A positive coffee-price shock produces heterogeneous real effects: the response of aggregate GDP is modest and short-lived, while agricultural GDP reacts more strongly and persistently. The shock propagates mainly through external and nominal channels—especially the exchange rate and terms of trade—whereas consumer-price pass-through is present but comparatively moderate. These findings contribute to the commodity-shock literature by providing sector-sensitive evidence for an agricultural export shock and by clarifying the mechanisms through which coffee-price movements propagate to domestic activity and prices in a small open agricultural economy. Full article

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article

This study investigates the impact of oil prices and exchange rates on the manufacturing output of small and medium-sized enterprises (SMEs) in Kazakhstan using data from the period 2000 to 2023, within the framework of the ARDL model. In the Kazakhstani economy, approximately 60% of SMEs operate in the wholesale and retail trade sectors, a factor that has been taken into consideration in interpreting the effects of macroeconomic variables on SME output. The results of the long-run analysis reveal that the exchange rate has a significant and strong positive effect on SME manufacturing output. Although oil prices do not directly exert a statistically significant influence on production output, the study identifies an indirect effect of oil revenues on SME output via the exchange rate channel. In the short-run findings, both exchange rates and oil prices are found to have significant effects on production output; in particular, oil prices exhibit a positive impact in the short term, which partially reverses in subsequent periods. The error correction term indicates a rapid adjustment back to equilibrium in the long run. These results highlight the high sensitivity of SME production performance in Kazakhstan to exchange rate fluctuations and underscore the indirect influence of oil prices through exchange rate movements. The study recommends enhancing the financial resilience of SMEs, minimizing exchange rate risks, and closely monitoring changes in energy prices. Furthermore, it suggests the development of policies aimed at promoting SMEs’ involvement in foreign currency-generating activities, as well as protecting enterprises in the wholesale and retail sectors against price volatility. In this context, the study makes a valuable contribution by providing a comprehensive evaluation of the effects of macroeconomic variables on SME manufacturing output. Full article

The present study provides a high-frequency empirical assessment of the financial performance, volatility, and market integration of thematic sustainability-oriented equity funds, focusing on clean energy and environmental innovation indices. Specifically, the study compares the financial performance of representative thematic green equity funds, such as ICLN and QCLN, and an emerging-market benchmark (ECON) with conventional developed-market indices (SPY, QQQ, GSPC, and XLE) using daily stock prices from 2010 to 2025. The analysis employs a transparent and replicable framework based on daily logarithmic and cumulative returns and incorporates the compound annual growth rate (CAGR), Sharpe and Sortino ratios, beta estimation, correlation analysis, and maximum drawdown. The research frequency is appropriate for a thorough analysis of short-term market structures and performance. The results indicate that sustainability-oriented equity indices exhibit higher volatility, deeper drawdowns, and greater sensitivity to broad market movements than conventional benchmarks. Sustainability-focused equity indices that emphasize clean energy exhibit higher market sensitivity (betas above 1) and strong correlations with traditional equity indices. Correlation and beta estimates suggest a high degree of integration with traditional equity markets, implying limited diversification benefits within an equity-only framework. Periods of relative outperformance appear to be associated with favorable policy conditions and energy market dynamics, but are not consistently sustained over the sample period. In addition, the overall results suggest that sustainability investments generate substantial environmental and social externalities. Risk-adjusted performance measures suggest weaker historical performance over the sample period relative to conventional benchmarks. These findings should be interpreted as a comparative historical assessment rather than a structural risk model. From a policy perspective, the findings suggest that stable and credible regulatory frameworks, including long-term climate policy support and investment-enabling institutions, may be important for improving the financial resilience and long-term viability of green equity instruments. From a sustainability transition perspective, the observed volatility and market dependence of sustainability-oriented equity indices may constrain their effectiveness as standalone market-based financing mechanisms without complementary institutional and policy support. Full article

This study examines whether international food price dynamics provide a reliable signal of undernourishment and human development outcomes relevant to the attainment of SDG 2 (Zero Hunger) by 2030. We apply wavelet coherence analysis to the FAO Food Price Index and the prevalence of undernourishment (SDG Indicator 2.1.1) over 2001–2023, testing statistical significance against an AR(1) red-noise null hypothesis. Hybrid ARIMA–Random Forest models generate probabilistic price forecasts through 2030. Despite strong raw coherence (R² ≈ 0.77), only 7.8% of time–frequency cells achieve statistical significance, indicating that apparent co-movement largely reflects autocorrelation rather than substantive dependence. Where significant coherence emerges, it concentrates at medium-run horizons (3–6 years), consistent with undernourishment as a habitual dietary adequacy measure linked to sustained affordability pressures affecting health, productivity, and human capital formation. Rolling correlation analysis reveals suggestive evidence of a regime change around 2012—from negative to positive correlation—coinciding with a slowdown in progress toward reducing hunger, although the 5-year rolling windows yield only 19 observations, limiting the power of formal structural break tests. Price forecasts exhibit rapidly widening confidence intervals (by ±131 index points by 2030), underscoring fundamental limits to predictability. The annual PoU series comprises only 23 observations, which constrains the estimation of long-run (8–12-year) wavelet cycles; results at those horizons should therefore be interpreted with caution. These findings caution against mechanistic inferences from global price indices to hunger and human development outcomes, redirecting policy emphasis toward domestic transmission channels and nutrition-sensitive safety nets. Full article

This study employs a TVP-VAR-BK-DY framework to examine volatility spillovers between China’s financial markets and strategic metal assets. To capture retail investor sentiment, we construct a sentiment index using an LLM knowledge distillation framework. Building on this index, the analysis further incorporates economic policy uncertainty to investigate the joint effects of retail investor sentiment and economic policy uncertainty on cross-market volatility spillovers. The results show that: (1) Price movements in certain assets exhibit leading effects, while metals with stronger financial characteristics generate more pronounced spillover effects. (2) The spillover structure between China’s financial markets and strategic metal assets displays substantial heterogeneity across time horizons and frequency bands. In the 1–5-day frequency band, the stock market serves as a net transmitter of volatility to the banking sector, gold, and copper. In the frequency band exceeding five days, these three assets exert reverse net spillover effects on the stock market. (3) The effects of retail investor sentiment and economic policy uncertainty on volatility spillovers differ significantly. The impact of retail investor sentiment is primarily concentrated in the 1–5-day frequency band, whereas economic policy uncertainty exhibits significant spillover effects in the frequency band exceeding six months. Full article

This study examines selected factors influencing white maize price movements in South Africa over the period 1994–2024. Given the importance of white maize for food security, understanding the drivers of producer price dynamics is essential for effective policy formulation and managing price stability. Annual time-series data are analysed using an Autoregressive Distributed Lag (ARDL) modelling framework, complemented by bounds testing, an error-correction model, Toda–Yamamoto causality and structural break tests. The bounds test confirms the existence of a stable long-run cointegrating relationship between maize prices and the selected explanatory variables. In the short run, imports and fuel prices exert significant upward pressure on maize producer prices, while lagged fuel prices and rainfall reduce prices. In the long run, imports and fuel prices remain statistically significant determinants, whereas maize production, exports, the exchange rate, and rainfall are insignificant. Complemented with the structural break tests that identify regime shifts in the early 2000s, 2012, and 2021, causality results indicate that imports, rainfall and fuel prices lead to Granger causality in maize producer prices. Collectively the findings reinforce the conclusion that white maize prices in South Africa are governed by long-run structural relationships, while short-run price movements reflect temporary adjustments rather than permanent shifts in market fundamentals. An integrated, long-horizon analysis that jointly incorporates climatic, trade, and macroeconomic determinants within an ARDL framework is provided by the study. Therefore, the findings have important implications for climate-risk management, transport cost containment, trade and price-stabilisation policies. Full article

This research examines market reactions to financial distress announcements by small privately held Canadian oil firms operating in the upstream sector between 2015 and 2021, employing an event study methodology, with daily spot prices for Brent and WTI crude oil serving as market benchmarks. The sample includes 11 firms that filed for insolvency, giving 99 observations for analysis. Data were collected from the publicly available Haynes Boone repository, ensuring transparency and verifiability. Abnormal returns were computed using market-adjusted returns to control for general market movements, isolating event-specific effects. The findings reveal statistically significant yet modest abnormal returns around the announcement day, indicating a measured market reaction. These results indicate that investors may partially anticipate such events and interpret them as potential restructuring opportunities rather than indicators of sector-wide collapse. The study underscores the importance of transparent disclosure and structured legal frameworks in moderating market volatility during financial distress. While the analysis is confined to short-term effects and small firms, it provides valuable insights into how financial distress in small upstream oil firms influences commodity markets, contributing new evidence to the literature on event studies and financial distress in energy markets, and offers implications for policymakers aiming to enhance market stability. Full article