Search Results (48)

This paper examines global equity market integration, commodity-price exposure, and volatility spillovers in Ghana’s frontier equity market. Using daily data from January 2011 to December 2025, we estimate a multi-factor asset pricing model nested within a GARCH framework for the Ghana Stock Exchange Composite Index (GSECI) and the Financial Sector Index (GSEFSI). The model jointly estimates first-moment return exposures and second-moment volatility spillovers from a global equity market and three key global commodity markets: gold, crude oil, and cocoa, while controlling for asymmetric volatility, return serial dependence, and domestic macro-financial shifts associated with banking sector recapitalization and the Domestic Debt Exchange Programme (DDEP). The Ghanaian equity market is exposed to the global equity market, indicating measurable but economically modest global integration, with stronger exposure in the financial sector. Commodity-price exposures are selective, with gold and crude oil exposures concentrated in the financial sector, whereas the cocoa factor is negatively associated with returns on both indices. The variance results show persistent volatility, inverse asymmetric volatility responses, and differentiated volatility spillovers from global equity and commodity markets. The DDEP period is associated with significant equity market repricing, particularly in the financial sector. These findings indicate that Ghana’s equity market dynamics are shaped jointly by global equity and commodity market information, frontier market frictions, and sovereign–bank conditions. Full article

Outliers in financial time series can reveal latent inter-asset relationships that are often missed by traditional dependence measures and average dynamic models. To address this gap, we propose Outlier-Driven Network Inference (ODNI), a framework for reconstructing directed lagged Tail Outlier-Triggering Networks from financial time series. ODNI first converts multivariate return series into upper and lower tail outlier indicators using empirical quantiles, then applies a bivariate EM-based attribution model to infer lagged triggering relationships across tail channels, and finally constructs a directed weighted network by combining baseline-corrected excess activation with EM attribution weights. For controlled evaluation, we simulate multivariate time series with volatility clustering and cross-variable spillovers from a known directed interaction template using a cross-GARCH(1,1) model. Across extensive experiments, ODNI achieves the best reconstruction performance among CoVaR, a Clayton copula tail-dependent network, and DCC-GARCH, with especially strong precision. Robustness tests show stable behavior across regimes, with systematic improvement as sample length increases and true coupling becomes more identifiable. Applications to major foreign exchange rates and global stock indices further reveal clear regional structure and asymmetric sender–receiver roles across tail-triggering channels. ODNI provides a practical tool for uncovering latent risk transmission pathways driven by tail outliers. 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

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

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

This paper examines tail risk spillovers and cross-market volatility forecasting between the U.S. equity market and the crude oil market. Using realized and implied volatility within a heterogeneous autoregressive (HAR) framework, we document asymmetric and time-varying tail risk transmission across the two markets. Motivated by these findings, we propose several cross-market volatility forecasting strategies, including direct information augmentation, threshold-based designs, forecast averaging, and transfer learning. The results show that incorporating cross-market information improves volatility forecasts primarily at medium and longer horizons, consistent with the forward-looking nature of implied volatility. Moreover, the relative effectiveness of different transmission mechanisms varies across markets, with transfer learning performing particularly well in the crude oil market. Overall, the findings highlight the importance of linking tail risk spillovers to volatility forecasting and demonstrate that flexible cross-market information transmission can enhance predictive performance across markets and horizons. Full article

Using a quantile–frequency connectedness framework, this study analyzes the regime-contingent and horizon-specific transmission of shocks among AI assets, FinTech markets, and Blue Economy financial instruments. The empirical results reveal a distinctly asymmetric connectedness structure, whereby high-frequency spillovers intensify in upper-quantile states associated with liquidity stress and sentiment-driven trading, while low-frequency connectedness remains comparatively muted, thereby preserving cross-segment diversification potential. AI assets emerge as dominant net transmitters in short-horizon dynamics, reflecting rapid innovation cycles and speculative adjustments. FinTech markets exhibit stabilizing properties under median regimes but transition into net propagation roles when risk conditions escalate. Blue finance instruments act as conditional net absorbers, attenuating volatility originating from digital innovation-driven markets, particularly during adverse market states. By decomposing spillover intensities across quantiles and spectral bands, the analysis highlights a structural differentiation between innovation-sensitive digital assets and the comparatively stable behavior of blue-themed financial assets. These findings advance the understanding of nonlinear dependence, asymmetric contagion, and state-dependent co-movements in emerging financial ecosystems. The results provide actionable insights for systemic-risk measurement, cross-market shock diagnostics, and multi-asset portfolio construction in an increasingly interconnected global financial system. Full article

This study explores how the volatility spillover mechanism and dynamic dependence among the founding BRICS equity markets, namely IBOVESPA, MICEX, Nifty 50, SSE, and JSE, have evolved over time using a multivariate DCC-GARCH model. The analysis is conducted across three distinct regimes: the pre-COVID-19 period (1 January 2010 to 10 March 2020), the COVID-19 crisis (11 March 2020 to 23 February 2022), and the Russia–Ukraine war and sanction period (24 February 2022 to 31 March 2024). The findings indicate that, prior to the COVID-19 pandemic, the BRICS equity markets experienced significant short-term volatility spillovers and significant volatility persistence, indicative of slow financial integration, as opposed to rapid contagion. In comparison, the COVID-19 pandemic resulted in significant structural shifts in the form of increased shock transmission, greater co-movement, and evident financial contagion among the markets. During the post-COVID-19 conflict period, while there was considerable persistence in volatility, the primary drivers of volatility spillovers were geopolitical. Across the three sub-periods, the volatility spillover network shows pronounced structural changes. Before COVID-19, IBOVESPA, MICEX, and SSE act as net transmitters, while Nifty 50 and JSE are net receivers. During the COVID-19 crisis, SSE and JSE become the main shock transmitters, whereas IBOVESPA, MICEX, and Nifty 50 shift to receiver roles. In the post-COVID-19 Russia–Ukraine war period, the network becomes more asymmetric, with JSE and Nifty 50 again emerging as net transmitters, while MICEX and SSE function primarily as net receivers. Overall, this study demonstrates that BRICS equity market interdependence is regime-specific and greatly dependent on exogenous global events. Full article

This study investigates cross-market dependence and dynamic hedging performance between the U.S. equity market and major commodity assets across distinct crisis regimes. Using daily data for the S&P 500 index and four key commodities (WTI crude oil, gold, wheat, and natural gas), we examine how market linkages evolve during systemic disruptions by applying Multifractal Detrended Cross-Correlation Analysis (MFCCA) and the q-dependent detrended correlation coefficient. Hedging performance is assessed using optimal hedge ratios estimated under two multivariate GARCH frameworks: the Asymmetric Dynamic Conditional Correlation (ADCC-GARCH) and the Generalized Orthogonal GARCH (GO-GARCH) model. The findings reveal strong multiscale and time-varying dependencies that intensify during high-volatility periods, reducing the benefits of conventional portfolio diversification. Hedging effectiveness proves to be regime dependent and strongly influenced by nonlinear cross-market interactions. The GO-GARCH model captures volatility spillovers and asymmetric co-movements more effectively, delivering superior hedging results compared with ADCC, especially during episodes of extreme market stress. Among the analysed commodities, crude oil and gold offer the most reliable hedging properties, whereas wheat and natural gas show unstable performance due to supply side shocks. These results emphasize the need for flexible, dynamically adjusted risk-management strategies during crisis environments. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article

Using a time-frequency and quantile connectedness approach, our study examines the complex return spillovers dynamics between BRICS Plus stock markets, the volatility index (VIX), and the global geopolitical risk index (GPRD). By employing advanced models such as TVP-VAR, quantile connectedness, and spectral decomposition, we demonstrate how these markets interact across different market conditions and periods. Our results indicate that the VIX consistently acts as the dominant net transmitter of shocks, especially during periods of heightened uncertainty such as the COVID-19 pandemic, the Russian-Ukraine conflict, and the Trump-era U.S.-China trade tensions. In contrast, the GPRD functions predominantly as a net receiver of shocks, indicating its potential role as a hedge during geopolitical crises. BRICS Plus markets exhibit heterogeneous behavior: Brazil, South Africa, and Russia frequently emerge as net transmitters, while China, India, Egypt, Saudi Arabia, and the UAE primarily act as net receivers. Spillovers are strongest at the extremes of the return distribution and are mainly driven by short-term dynamics, underscoring the importance of high-frequency reactions over persistent long-term effects. These findings highlight the asymmetric, nonlinear, and state-dependent nature of global financial contagion, offering important insights for risk management, asset allocation, and macroprudential policy design in emerging market contexts. 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

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

This paper investigates the dynamics of volatility spillovers in the South African foreign exchange market across calm and crisis periods, with particular attention paid to the pre- and post-COVID-19 eras. Employing daily exchange rate returns from 2015 to 2025, we apply a Quantile Vector Autoregression (QVAR) model to uncover asymmetries in spillover transmission across the distribution of returns. We evaluate the implications of these spillovers for portfolio performance under three canonical strategies: risk parity, tangency, and naïve equal-weighting. Our findings indicate that the COVID-19 shock intensified volatility spillovers and exacerbated their asymmetry, especially in the lower tail, while the pre-COVID period portrayed higher volatility compared to the post-COVID period under calm market conditions. While risk-based strategies dominate in tranquil markets, equal-weighted portfolios exhibit superior downside resilience under stress, although they ignore risk exposure. These results underscore the importance of accounting for tail-risk-driven interconnectedness in portfolio construction and risk management. This study contributes to the growing literature on volatility spillovers and offers practical insights for managing currency exposure in emerging markets under nonlinear dependence structures. Full article

This study examines volatility transmission between major European indices (CAC 40, DAX, FTSE MIB, IBEX 35, EURO STOXX 50) and Tunisia’s TUNINDEX amid global crises (2008 financial crisis, COVID-19, Russo-Ukrainian war). Using GARCH(1,1) and BEKK models, the analysis reveals low correlation and weak volatility spillovers between the TUNINDEX and European markets, indicating relative decoupling. ARCH-LM tests confirm conditional heteroskedasticity, while GARCH models show persistent volatility. The BEKK model underscores marginal shock transmission, affirming the TUNINDEX’s independence. These findings suggest diversification benefits for investors but highlight local risk considerations. Practical recommendations are provided for stakeholders, with future research directions including asymmetric effects and high-frequency data analysis. Full article