Search Results (136)

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

This research investigates the dynamic spillover effects among green bond markets and the impact of investor sentiment on these spillovers. We employ different research methods, including a time-varying parameter vector autoregression, an exponential general autoregressive conditional heteroscedasticity, and a generalized autoregressive conditional heteroskedasticity-mixed data sampling model. Our sample is for twelve international green bond markets from 3 January 2022 to 31 December 2024. Our results evidence the strong correlation between twelve green bond markets, with the United States and China being the net risk receivers and Sweden being the largest net shock transmitter. We also find the varied impact of direct and indirect investor sentiment on the net total directional spillovers. Our research offers fresh contributions to the existing literature in different ways. On the one hand, it adds to the green finance literature by clarifying the dynamic spillovers among leading international green bond markets. On the other hand, it extends behavioral finance research by including direct and indirect investor sentiment in the spillovers of domestic and foreign green bond markets. Our study is also significant to related stakeholders, including investors in their portfolio rebalancing and policymakers in stabilizing green bond markets. 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

This study proposes a hybrid forecasting model that integrates the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a Long Short-Term Memory (LSTM) neural network to estimate Value at Risk (VaR) in the Rwandan foreign exchange market. The model is designed to capture both volatility clustering and temporal dependencies in daily exchange rate returns. Using daily data on USD, EUR, and GBP from 2012 to 2024, we evaluate the model’s performance relative to standalone GARCH(1,1) and LSTM models. Empirical results show that the hybrid model improves VaR estimation accuracy by up to 10%, especially during periods of elevated market volatility. These improvements are validated through MSE, MAE, and backtesting statistics. The enhanced accuracy is particularly relevant in emerging markets, where exchange rate dynamics are highly nonlinear and sensitive to external shocks. The proposed approach offers practical insights for financial institutions and regulators seeking to improve market risk assessment in emerging economies. Full article

In this study, the Gumbel distribution is utilized to construct exact analytical representations for two pivotal measures in financial risk evaluation: Value at Risk (VaR) and Conditional Value at Risk (CVaR). These refined formulations are developed with the intention of offering resilient and practically implementable tools to address the complexities inherent in economic risk analysis. Moreover, the newly established expressions are seamlessly integrated into the GARCH modeling framework, thereby enriching its predictive capabilities. In order to verify both the practical relevance and theoretical soundness of the presented methodology, it is systematically employed regarding the daily return series of a varied portfolio of stocks. The outcomes of the numerical experiments provide compelling evidence of the approach’s reliability and effectiveness, emphasizing its suitability for advancing contemporary risk management strategies in financial environments. Full article

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility. Full article

To describe the stylized features of volatility comprehensively, this paper embeds the time-varying leverage effect of volatility into the Realized Generalized AutoRegressive Conditional Heteroskedasticity (RG) model and proposes a new volatility model with a time-varying leverage effect. The Quasi-Maximum Likelihood-Kernel (QML-K) method is proposed to approximate the density function of returns and to estimate the parameters in the new model. Under some mild regularity conditions, the asymptotic properties of the resulting estimators are achieved. Simulation studies demonstrate that the proposed model yields better performances than traditional RG models under different situations. Finally, the empirical analysis shows better finite sample performance of the estimation method and the new model on real data compared with existing methods. Full article

Extreme weather events are the primary driver of environmental, social, and governance (ESG) responsible investment or sustainable stocks, which are gaining popularity worldwide, including in Thailand. Nevertheless, the function of sustainable stocks remains an academic dispute and without satisfactory conclusion for decision-making of Thai investors. Thus, we adopt a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to examine the influence of Thailand sustainability investment on Thailand’s stock market and financial assets. The result indicates that Thailand sustainability investment lacks hedging functions and is classified as a weak safe-haven for consumer product stocks, bitcoin, and Thai baht. Consequently, Thailand sustainability investment provides a better alternative asset for risk diversification, although volatility is low compared to other financial assets and decreases during crises. Investors are advised to diversify their investment risks by adding Thailand sustainability investment to their portfolios during a bearish market. Full article

This paper develops a framework for quantifying risk by integrating analytical derivations of Value at Risk (VaR) and Conditional VaR (CVaR) under the chi-squared distribution with empirical modeling via Generalized Autoregressive Conditional Heteroskedasticity (GARCH) processes. We first establish closed-form expressions for VaR and CVaR under the chi-squared distribution, leveraging properties of the inverse regularized gamma function and its connection to the quantile of the distribution. We evaluate the proposed framework across multiple time windows to assess its stability and sensitivity to market regimes. Empirical results demonstrate the chi-squared-based VaR and CVaR, when coupled with GARCH volatility forecasts, particularly during periods of heightened market volatility. Full article

There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship between the U.S. dollar index, international grain futures prices, and spot prices in the context of Federal Reserve monetary policy adjustments from 2000 to 2023. The findings reveal that, first, under conditions of long-run cointegration, the U.S. dollar index exerts a strong pricing influence over international grain futures, while grain futures demonstrate a significant price discovery function over spot prices. Second, both international grain futures and spot markets exhibit asymmetric volatility, with price increases being more pronounced than decreases in response to external shocks. Additionally, the U.S. dollar index has a unidirectional and inverse influence on grain futures prices, while futures and spot prices interact bidirectionally and move in the same direction. This paper contributes to understanding the impact of Federal Reserve monetary policy adjustments on international food prices and offers policy insights for countries to manage food import risks and maintain price stability. Full article

This study examines the dynamic relationship between inflation, inflation uncertainty, and economic performance in Mexico using the Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M) and bivariate GARCH-in-mean (BGARCH-M) models. Based on monthly data from 1995 to 2019, the analysis estimates nominal uncertainty and evaluates its macroeconomic implications under Mexico’s inflation-targeting regime. The results indicate a significant and positive link between past inflation and future uncertainty, underscoring the importance of maintaining low and stable inflation to contain volatility. Furthermore, inflation uncertainty is found to exert a negative influence on economic performance, particularly in terms of output variability. However, the study does not find conclusive evidence that inflation uncertainty declined following the formal adoption of inflation targeting. These findings suggest that, while Mexico has achieved price stability, inflation uncertainty remains sensitive to external shocks and policy credibility. The study contributes to the broader literature by reassessing the effectiveness of inflation targeting in an increasingly globalized and volatile environment, offering important lessons for emerging economies managing external vulnerabilities and institutional constraints. Full article

The financial markets are highly influential and any change in the economy can be reflected in stock prices and thus have an impact on stock indices. The relationship between stock indices and the way they are affected by extreme phenomena is important for defining diversification strategies and analyzing market maturity. The purpose of this study is to examine the interdependence relationships between the main Euronext stock indices and any changes caused by an extreme event—the COVID-19 pandemic. Copula models are used to estimate the dependence relationships between stock indices pairs after estimating ARMA-GARCH models to remove the autoregressive and conditional heteroskedastic effects from the daily return time series. The financial interdependence structures show a symmetric relationship of influence between the indices, with the exception of the CAC40/ISEQ pair, where there was financial contagion. In the case of the AEX/OBX pair, the dynamics of dependence may have changed significantly in response to the pressure of the pandemic. On the other hand, the dominant influence of the CAC40 before and the AEX after the pandemic confirms that the size and age of these indices give them a benchmark position in the market. Finally, with the exception of the AEX/OBX and CAC40/ISEQ pairs, the interdependencies between the stock indices decreased from the pre- to the post-pandemic sub-period. This result suggests that the COVID-19 pandemic has weakened the correlation between the markets, making them more mature and independent, and less risky for investors. Full article

This study examines the volatility and hedging effectiveness of commodities, specifically gold and oil, on the Indian stock market, focusing on both aggregate and sectoral indices. Data have been collected from 1 January 2021 to 31 December 2024 to cover the post-COVID-19 period. Utilizing the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (ADCC-GARCH) model, we analyze the volatility spillovers and time-varying correlations between commodity and stock market returns. The analysis of spillover connectedness reveals that both commodities exhibit limited and inconsistent hedging potential. Gold demonstrates low and stable spillovers in most sectors, indicating its diminished role as a reliable safe-haven asset in Indian markets. Oil shows relatively higher but volatile spillover effects, particularly with sectors closely tied to energy and industrial activities, reflecting its dependence on external economic and geopolitical factors. This study contributes to the literature by providing a sector-specific perspective on commodity–stock market interactions, challenging conventional assumptions of hedging efficiency of gold and oil. It also emphasizes the need to explore alternative hedging mechanisms for risk management in the post-crisis phase. Full article

This study analyses the impact of the Geopolitical Risk Index (GPR) on the volatility of commodity futures returns from 4 January 2010 to 30 June 2023, using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models. It expands the research scope to include precious metals, agricultural products, energy, and industrial metals. The study differentiates between the impacts of geopolitical threat events and actions using GPRACT and GPRTHREAT indicators. Findings reveal that negative geopolitical shocks increase commodity returns’ volatility more than positive shocks. Specifically, gold, silver, and natural gas are negatively affected, while wheat, corn, soybeans, cotton, zinc, nickel, lead, WTI oil, and Brent oil experience positive effects. Platinum, cocoa, coffee, and copper show no significant impact. These insights highlight the importance of geopolitical risks on commodity market volatility and returns, aiding in risk management and portfolio diversification. Policymakers, financial market stakeholders, and investors can leverage these findings to better understand the GPR’s relationship with commodity markets and develop effective strategies. Full article