Search Results (92)

Under the background of climate change, the risk spillover within the energy system is constantly intensifying. Clarifying the coupling relationship between entities within the energy system can help policymakers propose more reasonable policy measures and strengthen risk prevention. To estimate the risk spillover of energy-related systems, this paper constructs five subsystems: the fossil fuel subsystem, the electricity subsystem, the green bond subsystem, the renewable energy subsystem, and the carbon subsystem. Then, a quantitative risk analysis is conducted on two major energy consumption/carbon emission entities, China and Europe, based on the DCC-GARCH-CoVaR method. The result shows that (1) Markets of the same type often have more significant dynamic correlations. Of these, the average dynamic correlation coefficient of GBI-CABI (the Chinese green bond subsystem) and FR-DE (the European electricity subsystem) are the largest, by 0.8552 and 0.7347. (2) The high correlation between energy markets results in serious risk contagion, and the overall risk spillover effect within the European energy system is about 2.6 times that within the Chinese energy system. Of these, EUA and CABI are the main risk connectors of each energy system. 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 study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO₂ emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers. Full article

The cryptocurrency market, known for its inherent volatility, has been significantly influenced by external shocks, particularly during periods of global crises such as the COVID-19 pandemic and the Russia–Ukraine war. This study investigates the volatility of the top seven cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), USD Coin (USDC), XRP, and Cardano (ADA)—from 1 January 2020 to 1 September 2024, employing a range of GARCH models (GARCH, EGARCH, TGARCH, and DCC-GARCH). This research aims to examine the persistence of leverage effects, volatility asymmetry, and the impact of past price fluctuations on future volatility, with a particular focus on how these dynamics were shaped by the pandemic and geopolitical tensions. The findings reveal that past price fluctuations had a limited impact on future volatility for most cryptocurrencies, although leverage effects became evident during market anomalies. Stablecoins (USDC and USDT) showed a distinct volatility pattern, reflecting their peg to the US Dollar, while platform-associated BNB demonstrated unique volatility characteristics. The results underscore the market’s sensitivity to price movements, highlighting the varying reactions of investor profiles across different cryptocurrencies. These insights contribute to understanding volatility transmission within the cryptocurrency market during times of crisis and offer important implications for market participants, particularly in the context of risk management strategies. Full article

This study explores the resilience of the Indian stock market in the face of global shocks in the post-pandemic era, focusing on its volatility dynamics and interconnections with international indices. Through a combination of Vector Autoregression (VAR), DCC-GARCH, and wavelet analysis, we analysed the time-varying relationships between the National Stock Exchange (NSE) of India and major global indices, including those from the U.S., Europe, Asia-Pacific, Hong Kong and Japan. Time series data of the selected indices have been collected for the period 1 January 2021 to 30 September 2024. Results reveal that while the NSE demonstrates resilience through rapid adjustments following shocks, it remains vulnerable to substantial spillover effects from markets such as the S&P 500 and European indices. Wavelet coherence analysis identifies periods of high correlation, particularly during major economic events, indicating that regional and global factors can periodically compromise market stability. Moreover, the DCC-GARCH results show a persistent but fluctuating correlation with specific markets, reflecting a connected and adaptive nature of the Indian market that is influenced by regional dynamics. This study emphasises the importance of strategic risk management. It highlights critical periods and indices that policymakers and investors should monitor closely to understand the economic resilience of the Indian financial market better. Further research could explore sector-specific impacts and the role of macroeconomic factors in shaping market responses. Full article

The goal of this study is to look into the dynamic relationship between stock prices, foreign portfolio investment, and financial development in the South African economy. Federal Reserve Economic Data (FRED) provided quarterly time series data from 1960 (Q1) to 2024 (Q2). This study uses a structural VAR estimation approach and dynamic conditional correlation (DCC GARCH model). The DCC GARCH approach displays time-varying correlations between stock prices, credit given to the private sector as a measure of financial growth, and foreign portfolio investments. The dynamic links between stock prices, financial development, and foreign private investment (FPI) are examined using the SVAR technique. Our findings show that a financial development shock encourages and provokes a substantial influx of foreign portfolio investment into the South African economy. This suggests that overseas portfolio investments react favorably and notably well to favorable shocks in the financial development process. We suggest that a stable financial system framework and lower credit costs would strengthen the impact of higher stock prices on private sector credit and guarantee that higher stock prices have a beneficial impact on other financial development metrics. Better financial development metrics, such as credit to the private sector, will therefore increase foreign portfolio investment. Full article

This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 in the United States on the Latin American stock market using a dynamic conditional correlation approach. The study covers the period from 2014 to 2020, divided into the pre-COVID-19 period (January 2014–February 2020) and the COVID-19 period (March 2020–November 2020), to examine the sudden change in average conditional correlation from one period to the next and identify the contagion effect. The contagion test showed significant contagion between the S&P 500 and Latin American indices, except for Argentina’s MERVAL. Additionally, we applied deep learning models, specifically LSTM, to predict market dynamics and changes in volatility as an early warning system. The results indicate that incorporating LSTM improved the accuracy of predicting dynamic correlations and provided early risk signals during the crisis. This suggests that combining DCC-GARCH with deep learning techniques is a powerful tool for predicting and managing financial risk in highly uncertain markets. Full article

The study examines the time-varying correlation and return spillover mechanism among developed (U.S.) and emerging (BRIC) stock markets during major crises from 2000 to 2023, namely the global financial crisis, COVID-19, and the Russia–Ukraine war. To do so, we used dynamic conditional correlation (DCC-GARCH) and time-varying parameter vector autoregression (TVP-VAR) models. This study finds that the nature of market crises plays a significant role in the interrelationship and return spillover mechanisms among the U.S. and BRIC stock markets. The interconnectedness of the stock markets was strengthened by crises such as the GFC and the COVID-19 pandemic. On the other hand, the Russia–Ukraine war temporarily disrupted the interrelationships between the markets. The study yields valuable insight to local and international investors in portfolio diversification and risk management strategies during market turbulence. Full article

This study explores the financialization of agricultural commodities, focusing on how financial derivatives and index funds impact the volatility of agro-food markets. Using a Dynamic Conditional Correlation (DCC) GARCH model, we analyze volatility spillovers among key agricultural commodities, particularly maize, and related financial assets over a sample period from 2007 to 2020. Our analysis includes major financial assets like Exchange-Traded Funds (ETFs), the S&P 500 index, and agribusiness corporations such as ADM and Bunge and the largest corn flour producer, GRUMA. The results indicate that financial speculation, especially via passive investments such as ETFs, has intensified price volatility in commodity futures, leading to a systemic risk increase within the sector. This study provides empirical evidence of increased market integration between the agro-food sector and financial markets, underscoring risks to food security and economic stability. We conclude with recommendations for regulatory actions to mitigate systemic risks posed by the growing financial influence in agricultural markets. Full article

In this study, we examine the critical question of whether global equity and bond assets (both green and non-green) offer effective hedging and safe haven properties against stock market risks in South Asia, with a focus on Bangladesh, India, Pakistan, and Sri Lanka. The increasing integration of global financial markets and the volatility experienced during recent economic crises raise important questions regarding the resilience of South Asian markets and the potential protective role of global assets. Drawing on methods like VaR and CVaR tail risk estimators, the DCC-GJR-GARCH time-varying connectedness approach, and cost-effectiveness tools for hedging, we analyze data spanning from 2014 to 2022 to assess these relationships comprehensively. Our findings demonstrate that stock markets in Bangladesh experience lower levels of downside risk in each quantile; however, safe haven properties from the global financial markets are effective for Bangladeshi, Indian, and Pakistani stock markets during the crisis period. Meanwhile, the Sri Lankan stock market neither receives hedging usefulness nor safe haven benefits from the same marketplaces. Additionally, global green assets, specifically green bond assets, are more reliable sources to ensure the safest investment for South Asian investors. Finally, the portfolio implications suggest that while traditional global equity assets offer ideal portfolio weights for South Asian investors, global equity and bond assets (both green and non-green) are the cheapest hedgers for equity investors, particularly in the Bangladeshi, Pakistani, and Sri Lankan stock markets. Moreover, these results hold significant implications for investors seeking to optimize portfolios and manage risk, as well as for policymakers aiming to strengthen regional market resilience. By clarifying the protective capacities of global assets, particularly green ones, our study contributes to a nuanced understanding of portfolio diversification and financial stability strategies within emerging markets in South Asia. Full article

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises. Full article

This paper proposes a new approach to estimating the minimum variance hedge ratio (MVHR) based on the wild bootstrap and evaluates the approach using a spectrum of conservative to aggressive alternative hedging strategies associated with the percentiles of the MVHR’s bootstrap distribution. This approach is suggested to be more informative and effective relative to the conventional method of hedging solely based on a single-point estimate. Furthermore, the percentile-based MVHRs are robust to influential outliers, non-normality, and unknown forms of heteroskedasticity. The bootstrap percentile-based hedging strategies’ effectiveness is compared with those from the naïve method and the asymmetric DCC-GARCH model for a range of financial assets and commodities. The bootstrap percentile-based hedging technique is identified to outperform its alternatives in terms of hedging effectiveness, downside risk, and return variability, suggesting its superiority to other methods in both the literature and in practice. Full article

In the past few decades, India’s tech industry has boomed, making it a leader in the digital world. Today, India has many big tech companies, well-trained software developers, and cutting-edge technology like AI and cloud computing. This success shows India’s innovative spirit and makes the country a good example for other developing nations. However, global portfolio managers often overlook potential diversification opportunities beyond India’s dynamic stock market. This study investigates the viability of MINT (Mexico, Indonesia, Nigeria, and Turkey) as diversification targets, specifically analyzing spillover effects and volatility dynamics between their stock markets and that of India. Leveraging vector autoregressions (VARs) and dynamic conditional correlation (DCC)–GARCH models, we uncover intricate relationships. Further, DCC–GARCH analysis reveals varying degrees of volatility spillover, offering valuable insights for risk management. Our findings suggest that MINT economies, particularly Mexico and Turkey, hold promise for Indian portfolio diversification. By strategically incorporating these markets, investors can potentially mitigate India-specific risks and enhance portfolio returns. We urge global portfolio managers to consider Turkey as a viable diversification avenue, acknowledging the nuanced market growth dynamics highlighted in this study. Full article

This study advances the understanding of the Preferred Habitat Model’s capacity to shed light on the inter-market transfer of mean returns and the diffusion of price volatility in Pakistani investment markets. It examines the extent to which returns in one market exert a systematic influence on returns across others under the potential sway of interest rate policy shifts, USD exchange rate volatility, and domestic inflation trends. Employing a methodological arsenal that includes the GARCH process, enhanced by Dynamic Conditional Correlations (DCC), as well as the Markov Switching Model, this research assesses the propagation of mean returns and volatility across markets. The analysis uncovers significant linkages between monetary policy and stock market indices, underscoring the profound impact of monetary policy on cross-market performance transmission. These insights are pivotal for regulators overseeing the nuanced interaction between monetary policy and market performance. They are crucial for local and international investors interested in developing economies, especially in Pakistan’s markets. Full article

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility. Full article