Search Results (298)

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 financial integration and contagion across South Asia’s emerging and frontier markets during the 2001–2013 period, encompassing both the global financial and Eurozone crises. Employing a multi-factor asset pricing model within an EGARCH framework, we disentangle systematic global exposures from idiosyncratic shocks originating in the U.S. and Eurozone. By formally testing for structural changes in both mean returns and conditional variance, we uncover a striking “integration-contagion paradox.” While frontier markets (Bangladesh, Nepal) appear segmented from global pricing signals in tranquil times, they remain acutely susceptible to second-moment volatility contagion during stress periods. In contrast, India exhibits strong systematic return integration yet remains relatively insulated from volatility cascades. These results challenge the conventional view that financial segmentation offers a robust shield against systemic risk, revealing that a lack of global integration does not immunize markets against the transmission of global uncertainty. Full article

This research examines the clustering structure and volatility spillover among steel-related products in monthly data from July 2004 to September 2025. Using various clustering methods, K-means, hierarchical techniques and market network analysis with correlations, four distinct marketing clusters have been identified: (1) US (United States) steel products, (2) global cyclical raw materials, (3) US iron ore market, and (4) global base metals. The overall volatility spillover index stands at 15.39%, exhibiting significant dynamics that vary over time, driven by major economic events, including the 2008 global financial crisis, the 2015 Chinese currency devaluation, the COVID-19 outbreak, the 2022 Ukrainian conflict, and the 2025 Trump trade tariffs. The primary driver of volatility in global trade is US carbon steel wire prices, while the largest net recipient of volatility shocks is the global copper price. These findings have key implications for understanding the global interconnectedness of steel markets in the current context. Full article

This study investigates the dynamics of Bitcoin’s realized volatility by extending the Heterogeneous Autoregressive (HAR) framework to incorporate external shocks from major financial and commodity markets, namely the NASDAQ-100, Brent crude oil, and gold. To capture potential asymmetries, external market returns are decomposed into positive and negative components. In addition, structural changes in volatility dynamics are examined using structural break tests. The empirical results reveal strong volatility persistence at the daily and weekly horizons, consistent with the HAR structure. Shocks associated with the NASDAQ and gold markets are significantly related to Bitcoin’s realized volatility, whereas the association with crude oil prices is limited. Moreover, both negative and positive gold-market shocks display stronger linkages in the post-2022 period, suggesting time variation in the volatility relationship between Bitcoin and gold. Full article

Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises. This study develops a flexible framework to analyze volatility transmission in U.S. commodity markets under multiple crisis regimes. We propose a Bayesian Structural Vector Autoregressive Mixture Normal (BSVAR-MIX) model that embeds finite normal mixtures within a mixture-based heteroskedastic structural VAR framework. The model combines generalized forecast error variance decomposition with posterior-probability weighting. Daily data for eight U.S. benchmark commodities across food, energy, and precious metals markets are examined over the 2008–2016 global financial crisis and the 2017–2025 multi-crisis period, including COVID-19 and the Russia–Ukraine conflict. The BSVAR-MIX framework provides a flexible descriptive setting for capturing multimodal shocks, heteroskedastic volatility states, and regime-dependent spillover patterns in commodity markets. Empirically, Gold and oil dominate systemic volatility transmission, soybeans amplify food–energy spillovers, while coal and wheat exhibit rising fragility under policy and geopolitical shocks. Assets commonly viewed as safe havens may contribute to systemic stress during extreme events. Overall, the framework offers a robust tool for structural shock identification and cross-commodity risk monitoring relevant to U.S. macroprudential policy. Full article

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

This study examines the dynamic interactions between oil price shocks, monetary policy, and non-oil output in Saudi Arabia using Vector Autoregressive Model (VAR), and quarterly data spanning 2010: Q1–2025: Q3. The study aims to provide policy-relevant insights through which external oil price shocks and domestic monetary policy shocks affect inflation and non-oil economic activity in the context of Saudi Arabia’s structural transformation under Vision 2030. The results show that global oil prices behave largely as exogenous shocks, with limited feedback from domestic monetary conditions, implying that monetary policy effectiveness operates primarily through inflation and domestic demand channels rather than through oil prices directly. The findings underscore the importance of gradual and predictable monetary tightening, coordinated with fiscal and macroprudential policies, to mitigate the indirect spillovers of oil price volatility on the non-oil sector. While monetary policy plays a stabilizing role by containing inflation and supporting macroeconomic balance, sustaining diversification and non-oil growth under Vision 2030 requires complementary measures, including targeted credit support, financial market deepening, and structural reforms that enhance productivity and private-sector investment. Full article

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

This paper constructs and estimates a novel two-sector Dynamic Stochastic General Equilibrium (DSGE) model to analyze the macroeconomics of Kazakhstan’s dual-energy structure, where a large fossil fuel sector coexists with an emerging renewable segment. The model’s key innovation is its integration of an endogenous, depletable oil stock and a dual-inflation Taylor-type rule, which together capture the specific transmission channels between hydrocarbon dependence and green investment. By differentiating between oil-driven and core inflation, the framework quantifies how oil price volatility transmits monetary conditions to the renewable sector. Bayesian estimation, using sectoral data from national accounts, reveals a pronounced asymmetry: oil stock/discovery dynamics and oil revenue fluctuations dominate macroeconomic volatility, while the renewable sector exhibits stable output but remains vulnerable to oil-driven monetary tightening transmitted mainly through indirect channels. The results indicate that Kazakhstan’s ongoing energy transition offers a stabilizing diversification benefit in principle but remains structurally constrained by macroeconomic dynamics and fiscal patterns anchored to hydrocarbon conditions. These findings provide a quantitative basis for designing transition policies that mitigate cross-sector spillovers and support effective diversification in resource-dependent economies. Full article

This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with applications in green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH variance equations, the model delivers a sparse and data-driven volatility spillover structure while preserving the positive definiteness of the conditional covariance matrix. Using daily data on green and conventional bonds, equities, energy commodities, and carbon allowances, we show that adaptive regularisation substantially reduces model complexity and improves economic interpretability relative to an unpenalised MGARCH benchmark. Out-of-sample forecasting experiments at multiple horizons demonstrate that the Adaptive LASSO-MGARCH model consistently achieves lower covariance forecast losses, and statistical tests based on the White reality check confirm that these improvements are significant across alternative loss functions. Full article

The objective of this study is to explore the time-varying shock transmission mechanism between aggregated financial stress indices (FSIs) of developed economies (the U.S., the U.K., the European Union (EU) and Japan) and the emerging economy of China. We employ a novel Time-Varying Parameter Vector Auto-Regression (TVP-VAR)-based “connectedness approach” to capture dynamic shock spillovers without the limitations of arbitrarily chosen rolling windows, loss of observations, or excessive sensitivity to outliers, as it is grounded in a multivariate Kalman filter structure. The aggregated measures of the FSIs of China, the U.S., the U.K., the EU and Japan are incorporated from the Asian Development Bank’s data repository by using time-series observations from January 2010 to September 2023. The findings indicate that the FSI of China is influenced by financial stress shocks originating from Japan (18.35%) and the U.S. (16.86%) the most, whereas the U.K. (EU) contributes to only 8.42% (6.54%) of FSI shocks in China. This research article significantly captures China’s heightened vulnerability to external financial stress shocks from developed economic systems and underscores the critical importance of reinforcing financial resilience, strengthening macro-prudential regulations and early-warning systems, and expanding financial buffers during episodes of trade uncertainty like restrictions on China’s rare earth exports and solar panels, U.S. restrictions on industrial metal imports, Brexit, supply chain disruptions amid COVID-19, and geopolitical uncertainties like the Russia–Ukraine war. Overall, this study provides actionable guidance for mitigating the impact of global financial stresses, improving risk management, and safeguarding economic stability in an increasingly interconnected and volatile international environment. Full article

Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool. Full article

Financial markets often display nonlinear and turbulent dynamics during periods of stress, and crude-oil and global equity systems frequently demonstrate closely connected forms of instability. Earlier studies report multifractality, chaotic features and regime-dependent spillovers across commodities and equities, yet existing approaches rarely succeed in capturing both the intrinsic complexity of oil-market behavior and the changing structure of cross-asset dependence. This limitation reduces the ability to distinguish calm from turbulent regimes and weakens short-horizon risk assessment. The present study introduces a unified framework that quantifies and predicts systemic instability within the coupled oil–equity system. The analysis constructs a crude-oil complexity index based on multifractal fluctuation analysis, permutation and approximate entropy, and Lyapunov-based indicators of chaotic dynamics. At the same time, it develops an information-theoretic network of global equity and energy-sector returns and summarizes its instability through measures of edge turnover, spectral radius, degree entropy and strength dispersion. These components are combined to form the Coupled Multiscale Chaos Index (CMCI), a scalar state variable that distinguishes calm, transitional and chaotic market regimes. Empirical results indicate that Brent and WTI exhibit pronounced multifractality, elevated entropy and positive Lyapunov exponents, while the dependence network becomes more centralized, more clustered and more capable of shock amplification during high-CMCI states. The CMCI moves closely with realized volatility and provides significant predictive content for five-day variance across major global equity benchmarks, with performance superior to models that rely only on macro-financial controls. Out-of-sample evaluation shows that forecasts incorporating measures of complexity record substantially lower MSE and QLIKE losses. The findings indicate that systemic instability reflects the interaction between local chaotic dynamics in crude-oil markets and turbulence in the global dependence network. The CMCI offers a practical early-warning indicator that supports risk management, forecasting and macroprudential supervision. Full article

Rising geopolitical tensions and fluctuating financial market conditions have increased volatility and negatively impacted property returns across BRICS countries, yet this critical area remains underexplored despite its significant implications for policy reform and investor participation. To this extent, the objective of the study is to examine the effect of geopolitical uncertainty on BRICS property market returns under changing market conditions. Using a Markov regime-switching model for the period February 2011 to June 2025, the findings reveal regime-specific effects. That being said, Brazil’s property market returns are affected positively (negatively) by South Africa’s (China’s) geopolitical uncertainty, whereas India’s and South Africa’s property market returns are affected negatively and positively by Russia’s geopolitical uncertainty, respectively. These findings were further evident in the bear market condition, as Russia’s geopolitical uncertainty has a significant negative effect on Brazil’s property market returns. Similarly, BRICS countries’ returns are dominated by bear market conditions, revealing negative returns, suggesting the BRICS property market returns are less resilient to periods of uncertainty. The findings underscore the need for new policy reforms to regulate BRICS members’ participation and minimize spillover effects, while investors should closely monitor geopolitical uncertainty within BRICS countries to manage return prospects effectively. Full article

Regional financial integration in East Africa remains shallow, yet contagion risks persist due to market fragility and illiquidity. Using daily data from 2014 to 2025 from the Nairobi Securities Exchange (NSE), Dar es Salaam Stock Exchange (DSE), Rwanda Stock Exchange (RSE), and Uganda Securities Exchange (USE), this study examines volatility spillovers, dynamic connectedness, and contagion through autoregressive moving average – generalised autoregressive conditional heteroscedasticity (ARMA–GARCH) diagnostics, asymmetric dynamic conditional correlation (ADCC–GARCH) correlations, and the Diebold–Yilmaz framework. The results show weak spillovers and limited connectedness in tranquil periods, reflecting persistent segmentation. However, systemic stress triggers abnormal surges in correlations and connectedness, consistent with contagion as a temporary amplification of cross-market linkages. The NSE emerges as the dominant transmitter, driven by liquidity and cross-listings, while the USE acts as a passive absorber. The RSE and DSE alternate between marginal transmitters and receivers depending on conditions. These findings support the Adaptive Market and Financial Instability Hypotheses, underscoring the need for harmonised regulation, liquidity reforms, and adaptive risk management to bolster resilience. Full article