Search Results (108)

This study investigates the volatility nexus between exchange rates, interest rates, and stock market returns in South Africa, an emerging economy characterised by deep financial integration and exposure to global capital flows. Using monthly data from January 2003 to February 2025, the analysis employs a multi-layered econometric framework combining asymmetric GARCH models (EGARCH and GJR-GARCH), an Asymmetric Dynamic Conditional Correlation (ADCC-GARCH) specification, and a GARCH-MIDAS–DCC approach that decomposes volatility into long-run and short-run components while modelling time-varying cross-market dependence. The findings indicate that exchange rate volatility is the dominant and most persistent driver of financial market risk, highlighting the central role of the South African rand in transmitting global shocks to domestic markets. Equity market volatility is largely shock driven and mean reverting, with sharp increases during major crisis episodes such as the Global Financial Crisis and the COVID-19 pandemic. Dynamic correlations across markets are persistent but predominantly negative between stock returns and exchange rates, while linkages involving interest rates are weaker and more episodic. Overall, the results suggest that South Africa’s financial volatility nexus operates primarily through exchange rate-driven transmission rather than short-run contagion effects. Full article

The main purpose of this study was to analyze the dynamics of the conditional correlation between Bitcoin and BOVA11 (a Brazilian stock market ETF that has seen a significant increase in foreign investors) across the pre-, during, and post-COVID-19 pandemic periods. This analysis allowed us to investigate the Bitcoin characteristics as a diversifier, hedge, or safe haven relative to the ETF. The study employed a DCC-GARCH model using daily closing prices from 2 January 2015 to 26 September 2025. A robustness check was conducted using Large Language Models (LLMs). Results indicated that in the pre- and post-pandemic periods, Bitcoin showed no significant correlation with the ETF, potentially acting as a weak hedge. Conversely, during the pandemic, Bitcoin behaved as a diversifier for the ETF rather than a safe haven. This finding may surprise market participants, particularly given the widespread narrative of Bitcoin as “digital gold” and, therefore, a natural protection in scenarios of high uncertainty. The results suggest that, during the pandemic, Bitcoin’s behavior aligned more closely with risk assets than with safe havens, underscoring the need for cautious, context-specific empirical assessments of its protective properties. 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

In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoin’s extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoin’s returns exhibited a negative relationship with Bitcoin’s extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation. Full article

Pan-Homophonic events denote fluctuations in stock prices that are triggered by phonetic similarities between event keywords and stock tickers. As a relatively novel and under-researched phenomenon, they mirror a subtle yet influential behavioral deviation within financial markets. Centering on the case of Chuandazhisheng, this study delves into how such events produce dynamic and time-varying impacts on stock prices. A linguistic amplitude segmentation method is devised to discriminate between high- and low-intensity events based on information entropy. To separate pan-homophonic-driven price movements from broader market trends, the Relational Stock Ranking (RSR) model is integrated with a Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework to establish an adjusted price benchmark. The empirical analysis reveals a sequential price response: initial moderate fluctuations in the low-amplitude phase often yield to more prominent volatility in the high-amplitude phase. While price surges typically occur within one or two days of the event, they generally revert within approximately three weeks. Moreover, repeated exposures to homo- phonic stimuli seem to attenuate the response, indicating a decaying spillover pattern. These findings contribute to a more profound understanding of the intersection between linguistic cues and market behavior and provide practical insights for investor education, information filtering, and regulatory supervision. Full article

Accurately forecasting the dynamic linkages among carbon, energy, and stock markets is essential for effective risk management and the design of energy transition strategies. This study proposes a BOHB-optimized Multivariable Graph Neural Network (BOHB-MSGNN) framework to forecast dynamic correlations derived from a DCC-GARCH model. Using data from the EU ETS market and related energy and stock markets, we document strong and persistent interconnectedness across markets, with the carbon market exhibiting the closest linkage to natural gas, followed by coal, stocks, and oil. Moreover, the proposed BOHB-MSGNN model significantly outperforms benchmark models in predicting dynamic risk correlations across multiple error metrics, owing to its ability to capture both intra-series and inter-series dependencies. Minimum-variance portfolios based on predicted correlations achieve returns similar to those using realized correlations. Forecasts also suggest a moderate decline in future correlations, highlighting diversification opportunities. These results offer practical implications for portfolio allocation, risk management, and carbon market policy. Full article

This study investigates risk contagion and dependence structures between U.S. and Chinese technology-related stock markets, focusing on the electronics and semiconductor sectors. We employ DCC-GARCH models to capture time-varying correlations and copula models to analyze nonlinear and tail dependencies. To highlight extreme risk dynamics, we extend the analysis to Value-at-Risk (VaR) series derived from a GARCH(1,1)-Skewed-t model. Empirical results reveal three major findings. First, volatility clustering and negative skewness are evident across markets, with extreme downside risks concentrated during the 2015 Chinese stock market crash and the 2020 COVID-19 pandemic. Second, copula results show stronger upper-tail dependence in cross-border broad markets and more symmetric dependence within domestic Chinese markets, while U.S. sectoral linkages exhibit the highest vulnerability during downturns. Third, dynamic copula analysis indicates that downside contagion is episodic and crisis-driven, whereas rebound co-movements are structurally persistent. These findings contribute to understanding systemic vulnerability in global technology markets. They provide insights for investors, regulators, and policymakers on monitoring cross-market contagion and managing systemic risk under stress scenarios. Full article

This paper analyzes the effectiveness of some defensive assets inside global stock portfolios by applying a standard VaR approach to daily data from 2021 to 2024. The 5Y US note is by far the best hedging instrument for single-hedged portfolios, while in multiple-hedged portfolios further VaR reductions are obtained including commodities, utilities, and real estate stocks. Bitcoin’s hedging performance is strongly negative, displaying an average VaR difference of more than two basis points with respect to the best-performing multiple-hedged portfolio in moderately defensive scenarios. This gap implies much higher maximum potential daily losses for Bitcoin’s single-hedged portfolios. Dynamic risk profiles of multiple-hedged portfolios display a smoother pattern than single-hedged portfolios, particularly during turbulent periods corresponding to the start of the Russia–Ukraine war, emphasizing the crucial benefits of higher asset diversification. Full article

Price volatility in the South African fresh produce market poses significant risks to the entire value chain. This study examines the extent of price volatility and spillover effects in these markets to improve price risk management and enhance market stability. Using weekly price data for eight major vegetables (cabbages, carrots, garlic, onions, potatoes, sweet potatoes, spinach, and tomatoes) collected from 19 regional fresh produce markets, volatility patterns were initially assessed with descriptive statistics. Time-varying volatility persistence was modelled using ARCH and GARCH frameworks. The DCC-GARCH framework was used to evaluate spillover effects between markets, and cointegration analysis is employed to determine both short- and long-run interdependencies. The results confirm the existence of spillover effects and patterns of price volatility in the fresh produce markets. We found volatility spillovers between key regional markets. For example, Johannesburg and Tshwane fresh produce markets (large central markets) transmit to several smaller markets, as indicated by significant DCC-GARCH spillover coefficients. Cointegration results show the partial integration of fresh produce markets, suggesting that price movements and volatility are interconnected across regions. This empirical result underscores the importance of understanding price risk management strategies in fresh produce markets and helps value chain decision makers better understand, anticipate, or test the possible effects of price volatility in fresh produce markets at any given time. Policy makers and other stakeholders in the value chain are equipped with knowledge of how best to serve society. Full article

The urgent need to mitigate climate change has elevated green hydrogen as a sustainable alternative to fossil fuels, while green cryptocurrencies have emerged to address the environmental concerns of traditional cryptocurrency mining. This study investigates the dynamic correlation between the green hydrogen market and selected green cryptocurrencies (Cardano, Stellar, Hedera, Algorand, and Chia) from July 2021 to April 2024, utilizing the Dynamic Conditional Correlation GARCH (DCC-GARCH) model with robustness checks using EGARCH and GJR-GARCH specifications. Our findings reveal significant correlations, with peaks reaching up to 50% in 2022, a period likely influenced by the Russia-Ukraine conflict. Subsequently, a decline in these correlations was observed in 2023. These results underscore the interconnectedness of sustainability-driven markets, suggesting potential contagion effects during periods of global instability. The high persistence of correlation shocks (α + β values approaching unity) indicates that correlation regimes tend to be long- lasting, with important implications for portfolio diversification and risk management strategies. Robustness checks using EGARCH and GJR-GARCH specifications confirmed qualitatively similar patterns, reinforcing the validity of our findings into the evolving landscape of green finance and energy. Full article

Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios. Full article

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development. Full article

Food price volatility is a persistent challenge in Indonesia, where agriculture is central to food security and rural livelihoods. While price transmission has been studied, little is known about how volatility spreads sub-nationally in archipelagic economies with fragmented infrastructure. This study applies a Dynamic Conditional Correlation GARCH (DCC-GARCH) model to monthly rural producer price data from 2009 to 2022 for six commodities: rice, chicken, eggs, chili, cayenne, and shallots. Results show that Java functions as the core volatility transmitter, with long-run conditional correlations exceeding 0.92 in Sumatra, 0.91 in Kalimantan, and 0.90 in Papua, reflecting strong and persistent co-movements. Even in low-production regions such as Maluku, significant volatility linkages reveal structural dependence on Java. Volatility clustering is particularly intense for perishables like chili and shallots. The findings highlight the need for spatially differentiated stabilization policies, including upstream interventions in Java and cooperative-based storage systems in outer islands. This study is the first to apply a DCC-GARCH framework to rural producer price data in an archipelagic context, capturing volatility transmission across regions. Its novelty lies in linking these spillovers with regional market dependence, offering new empirical evidence and actionable insights for designing inclusive and geographically responsive food security strategies. Full article

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring the extent of shock spillover between selected developed and emerging markets during idiosyncratic crisis and normal periods. The US and EU are identified as developed economies. However, emerging markets are classified by regions to determine whether their responses to shocks from developed economies are homogeneous or heterogeneous depending on the region to which they belong. The suggested entropy test is based on the conditional correlations obtained from an asymmetric dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (A-DCC GARCH) model. In addition to economic methods, statistical methods based on the regime-switching technique are used to date the different phases of the global financial crisis (GFC) and the European sovereign debt crisis (ESDC). Our findings show that all emerging markets decoupled from developed economies in at least one of the phases of the two crises. These findings provide valuable insights for policymakers, investors, and asset managers for portfolio allocation and financial regulations. Full article

Using high-frequency financial data, this study investigates volatility spillovers between five renewable energy subsectors (wind, solar, geothermal, bioenergy, and fuel cells), five conventional energy markets (oil, gas, coal, uranium, and gasoline), and carbon emissions for five industrial sectors (power, industry, ground transportation, domestic aviation, and residential) based on a Diebold–Yilmaz VAR-based spillover framework. The results document that the industry and power sectors are the key players in the transmission effects of carbon shocks. In contrast, the reverse is true for the residential and aviation sectors. For renewable energy, fuel cells, and geothermal power, strong forward linkages appear to significantly reduce carbon emissions, while reverse linkages that increase carbon emissions in response to shocks in clean-energy and carbon-intensive industries are relatively high for coal and oil. We also find that the total volatility connectedness exceeds 84%, indicating significant systemic risk transmission. The clean-energy subsectors, particularly wind and solar, now compete in fossil-fuel markets during geopolitical crises. Applying the DCC-GARCH t-copula method to assess portfolio hedging strategies, we find that fuel cell and geothermal assets are the most effective in hedging against volatility in fossil-fuel prices. In contrast, nuclear and gas assets provide benefits from diversification. These results underscore the growing strategic importance of clean energy in mitigating sector-specific emission risks and fostering resilient energy systems in alignment with the United States’ net-zero carbon goals. Full article