Search Results (96)

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

This study investigates the volatility dynamics of the Bahrain All Share Index (BAX) between 2010 and 2025, a period marked by COVID-19 and regional geopolitical shocks. Using ARMA (1,1) to model returns and four GARCH-family models (ARCH, GARCH, EGARCH, GJR-GARCH) to capture volatility, we provide new evidence from a bank-based frontier market that has received limited empirical attention. The results reveal that returns are stationary and exhibit volatility clustering. Among the competing models, EGARCH (1,1) provides the best fit—exhibiting the lowest AIC and SIC values and the highest log-likelihood—revealing a significant leverage effect whereby negative shocks generate stronger volatility than positive shocks. This asymmetric volatility pattern contradicts earlier findings for Bahrain but aligns with theoretical expectations for bank-based financial systems. The findings carry implications for investors in terms of portfolio risk management, derivative pricing, and asset allocation. They also have important implications for regulators and policymakers, suggesting that counter-cyclical buffers and interest rate adjustments could be applied to stabilize the market in anticipation of negative shocks. These insights enrich the scarce literature on volatility in small frontier markets and contribute to a more nuanced understanding of the volatility dynamics in the MENA region. Full article

This paper studies the volatility dynamics of the JSE Top40 Index by estimating a univariate GAS model with time-varying location, scale, and shape parameters (identity score scaling) and comparing its density and point-forecast performance against a stand-alone ARMA(3,2)–EGARCH(1,1) model and a hybrid ARMA(3,2)–EGARCH(1,1)–XGBoost framework. The GAS model is estimated on 3515 daily observations, and several conditional densities are examined. The Student-t GAS model (GAS–STD) obtains the lowest information criteria within the GAS family (AIC = 10,188.142; BIC = 10,243.626) and exhibits statistically significant persistence in location and scale dynamics. Statistical diagnostics provide evidence of correct density calibration (normalised log score = 1.1932; Uniform score = 0.4417), although residual skewness remains (IID-Test skewness $p=0.0134$). Out-of-sample analysis shows that GAS–STD performs strongly in density and risk forecasting, producing accurate 5% VaR and ES paths and passing coverage backtests (Kupiec LRuc $p=0.8414$; DQ $p=0.2281$). However, short-horizon point forecasts are most accurately produced by the Hybrid ARMA(3,2)–EGARCH(1,1)–XGBoost model (RMSE = 0.1386). The full Diebold-Mariano (DM) test confirms that all pairwise differences in predictive accuracy are statistically significant, and the model confidence set (MCS) procedure identifies the Hybrid model as the sole superior model at the 5% significance level, indicating that both ARMA(3,2)–EGARCH(1,1) and GAS–STD are statistically inferior. Simulation experiments illustrate that the tail behaviour of the Student-t distribution is sensitive to the degrees-of-freedom parameter $\nu$. For example, a Student-t distribution with $\nu =5$ exhibits total kurtosis of approximately $7.32$, indicating heavier tails compared to the Gaussian distribution. Overall, GAS–STD is a strong density and risk model for the JSE Top40, while the hybrid framework excels in short-term volatility forecasting. Full article

This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques. Full article

The systematic failure of standard Value-at-Risk (VaR) models for the Tokyo Commodity Exchange (TOCOM) rubber futures contract poses significant challenges for risk management. This study addresses the issue by examining the market’s split trading sessions, which induce distinct overnight and intraday volatility regimes. We decompose daily returns into these two components and apply tailored Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models. Our empirical results, strengthened by extensive robustness checks using EGARCH, IGARCH, and GJR-GARCH specifications, reveal that intraday volatility is persistent and influenced by leverage effects, whereas overnight volatility behaves as a jump-driven process unaccounted for by conventional models. Comprehensive VaR backtesting confirms that while traditional models accurately capture intraday risk, all standard daily models—including asymmetric variants—systematically and severely underestimate overnight risk. These findings demonstrate that aggregating returns into a single daily series conflates different volatility dynamics, leading to model failures. We propose a two-tiered risk management framework that separately applies conventional models to intraday risk and jump-aware measures for overnight risk. This approach aligns risk assessment with underlying market microstructure, improving model validity and capital adequacy for TOCOM rubber futures. 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

This paper examines the impact of the COVID-19 pandemic on the volatility of the EUR/MRU and USD/MRU exchange rates using GARCH-type models. Symmetric GARCH(1,1) and asymmetric specifications—EGARCH and GJR-GARCH—are applied to capture potential leverage effects over two periods: pre-COVID (January 2017–December 2019) and COVID (January 2017–December 2021). The results indicate that the pandemic increased short-run volatility for EUR/MRU, while its impact on USD/MRU was comparatively weaker. Asymmetric models reveal that COVID-19 altered the response of volatility to shocks, with EUR/MRU exhibiting heightened sensitivity and USD/MRU showing contrasting asymmetries. In addition, an out-of-sample backtesting exercise confirms the superior predictive performance of asymmetric models, particularly EGARCH for EUR/MRU and GJR-GARCH for USD/MRU. These findings underscore distinct volatility dynamics and the transmission of external shocks in a small open economy during periods of global uncertainty. Full article

This paper examines the high-frequency impact of tariff rhetoric on financial markets, a topic largely unexplored in existing literature. Unlike previous studies that focus on the long-term, macroeconomic effects of enacted trade policies, our research utilizes a novel, sentiment-based proxy variable for non-legislated tariff announcements. We demonstrate that political communication itself—not just formal policy changes—is a potent source of investor uncertainty and market volatility. Our analysis, employing a multi-model framework including VAR and EGARCH models, reveals several key findings. We find that trade-related shocks contribute significantly to market volatility by altering investor expectations and increasing perceived risk. A key discovery is a unique unidirectional causality where shocks to the S&P 500 preceded changes in our tariff variable, suggesting that market movements can influence policy rhetoric. Furthermore, our EGARCH analysis uncovers distinct volatility characteristics across asset classes, including an atypical positive asymmetry in the Chinese CSI 300. These results collectively provide robust empirical evidence that tariff rhetoric has a measurable and significant impact on asset prices and disproportionately increases market volatility, highlighting the need for policymakers to consider the financial market implications of their public statements. Full article

This study proposes a hybrid EGARCH-Informer framework for forecasting volatility and calibrating tail risk in financial time series. The econometric layer (EGARCH) captures asymmetric and persistent volatility dynamics, while the attention layer (Informer) models long-range temporal dependence with sparse attention. The framework produces horizon-specific forecasts (H = 1 and H = 5) that are mapped to VaR and CVaR at α = 0.95 and 0.99. Evaluation covers pointwise accuracy (MAE, RMSE) and risk coverage calibration (CVaR bias and Kupiec’s unconditional coverage), complemented by Conditional Coverage (CC) and Dynamic Quantile (DQ) diagnostics, and distributional robustness via a Student-t mapping of VaR/CVaR. Across four U.S. equity indices (SPX, IXIC, DJI, SOX), the hybrid matches GARCH at the short horizon and yields systematic error gains at the longer horizon while maintaining higher calibration quality than deep learning baselines. MAE and RMSE values remain near 0.0002 at H = 1, with relative improvements of 2–6% at H = 5. CVaR bias stays tightly bounded; DQ rarely rejects, and CC is stricter but consistent with clustered exceedances, and the Student-t results keep the median hit rates near nominal with small, mildly conservative CVaR biases. These findings confirm the hybrid model’s robustness and transferability across market conditions. Full article

This study examines the dynamics of equity market volatility and economic policy uncertainty (EPU) in South Africa during the COVID-19 pandemic. Using daily return data for sectoral indices and the JSE All Share Index (ALSI) from 1 January 2020 to 31 March 2022, the analysis explores both market-wide and sector-specific volatility responses. Univariate GARCH-family models (GARCH (1,1), E-GARCH, and T-GARCH) are employed to capture volatility clustering, persistence, and asymmetry across sectors. The results show that volatility was highly persistent during the pandemic, with sectoral differences in sensitivity to shocks: Consumer Staples and Financials were particularly reactive to recent news, while Health Care and Basic Materials were more stable. Asymmetric models confirm that market sentiment was predominantly driven by negative news, except in the Energy sector, where positive recovery signals played a stronger role. Correlation analysis further indicates that most sectors were moderately correlated with the ALSI, while Energy and Health Care behaved more independently. In contrast, both the ALSI and sector returns exhibited weak and negative correlations with the South African EPU index, suggesting that uncertainty did not translate directly into equity market declines. Overall, the findings highlight the importance of sectoral heterogeneity in volatility dynamics and suggest that during extreme market events, investors can mitigate downside risk by reallocating portfolios toward more resilient sectors. Full article

Volatility modelling is a key feature of financial risk management, portfolio optimisation, and forecasting, particularly for market indices such as the JSE Top40 Index, which serves as a benchmark for the South African stock market. This study investigates volatility modelling of the JSE Top40 Index log-returns from 2011 to 2025 using a hybrid approach that integrates statistical and machine learning techniques through a two-step approach. The ARMA(3,2) model was chosen as the optimal mean model, using the auto.arima() function from the forecast package in R (version 4.4.0). Several alternative variants of GARCH models, including sGARCH(1,1), GJR-GARCH(1,1), and EGARCH(1,1), were fitted under various conditional error distributions (i.e., STD, SSTD, GED, SGED, and GHD). The choice of the model was based on AIC, BIC, HQIC, and LL evaluation criteria, and ARMA(3,2)-EGARCH(1,1) was the best model according to the lowest evaluation criteria. Residual diagnostic results indicated that the model adequately captured autocorrelation, conditional heteroskedasticity, and asymmetry in JSE Top40 log-returns. Volatility persistence was also detected, confirming the persistence attributes of financial volatility. Thereafter, the ARMA(3,2)-EGARCH(1,1) model was coupled with XGBoost using standardised residuals extracted from ARMA(3,2)-EGARCH(1,1) as lagged features. The data was split into training (60%), testing (20%), and calibration (20%) sets. Based on the lowest values of forecast accuracy measures (i.e., MASE, RMSE, MAE, MAPE, and sMAPE), along with prediction intervals and their evaluation metrics (i.e., PICP, PINAW, PICAW, and PINAD), the hybrid model captured residual nonlinearities left by the standalone ARMA(3,2)-EGARCH(1,1) and demonstrated improved forecasting accuracy. The hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model outperforms the standalone ARMA(3,2)-EGARCH(1,1) model across all forecast accuracy measures. This highlights the robustness and suitability of the hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model for financial risk management in emerging markets and signifies the strengths of integrating statistical and machine learning methods in financial time series modelling. Full article

The South African exchange rate has been volatile in recent years affecting the competitiveness of commodities in the market. Consequently, South African agricultural exporters have faced lower profitability or entire losses. More South Africa is among the top agricultural exporters in Africa. Thus, the purpose of this study was to examine the effect of exchange rate volatility on agricultural exports in South Africa using the Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) model over the period extending from first quarter of 2013 to first quarter of 2024. The study finds that the exchange rate affects agricultural export negatively in South Africa. The findings display that the exchange rate is statistically significant in explaining agricultural exports in South Africa. In addition, this study finds interest rate affects agricultural exports negatively whereas investment and trade openness affect agricultural export positively in South Africa. This infers that agricultural exports in South Africa are explained by various economic factors. Therefore, this study proposes implementing currency stabilisation policies is a crucial strategy to reduce exchange rate volatility, thereby reducing the negative impact on agricultural exports in South Africa. The policymakers can use currency hedging as tool to lessen the negative impact associated with the exchange rate volatility. Full article

This study is devoted to investigating the Lunar New Year effect on market returns for the Ho Chi Minh Stock Exchange (HOSE). The data employed in this study include a daily series of the VN30-Index, which is a market capitalization weighted index of 30 large capitalization and high liquidity stocks traded on the HOSE, for the period from 6 February 2012 to 31 December 2024. The empirical findings derived from ordinary least squares (OLS), exponential-generalized autoregressive conditional heteroskedasticity [EGARCH(1,1)] regression models consistently confirm that the average return in the last two days and five days before the Lunar New Year are significantly higher than the average market returns on other days of the year. However, this study finds that the average return during the first two trading days and five trading days following the Lunar New Year are not significantly different from the average market returns on other days throughout the year. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

Global financial markets have experienced significant volatility during crises, particularly COVID-19 and the Russia–Ukraine conflict, prompting questions about how regional markets respond to such shocks. Previous research highlights the influence of crises on stock market volatility, focusing on individual events or global markets, but less is known about the comparative dynamics within the Gulf Cooperation Council (GCC) markets. Our study investigated volatility and asymmetric behavior within GCC stock markets during both crises. Furthermore, the econometric model E-GARCH(1,1) was applied to the daily frequency data of financial stock market returns from 11 March 2020 to 31 July 2023. This study examined volatility fluctuation patterns and provides a comparative assessment of GCC stock markets’ behavior during crises. Our findings reveal varying degrees of market volatility across the region during the COVID-19 crisis, with Qatar and the UAE exhibiting the highest levels of volatility persistence. In contrast, the Russia–Ukraine conflict has had a distinct effect on GCC markets, with Oman exhibiting the highest volatility persistence and Kuwait having the lowest volatility persistence. This study provides significant insights for policymakers and investors in managing risk and enhancing market resilience during economic and geopolitical uncertainty. Full article