Search Results (21)

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we use the GARCH and MGARCH models to assess the impact of these metrics on the volatility of oil prices, and the spillover effects between oil prices with these three metrics as exogenous shocks. Our result indicates (i) global oil price is negatively affected by GPRT at a moderate level of risks in longer time intervals; (ii) GPR, EPU, and VIX affect oil price’s volatility, and (iii) there exists a stronger long-persistent spillover effect between BRENT and DUBAI, with these metrics as exogenous shocks, while WTI is not affected. 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

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets. Full article

The stock market operates on informed decisions based on information gathered from heterogeneous sources, encompassing diverse beliefs, strategies, and knowledge. This study examines the validity of rational bubbles in stock market prices, focusing on eight African stock markets: South Africa, Nigeria, Kenya, Egypt, Morocco, Mauritius, Ghana, and Botswana. Utilizing newly developed econophysics-based unit root tests and the Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC MGARCH) models, the authors analyzed daily data from 1996 to 2022. Our findings indicate that these markets experienced bubbles at various points, often followed by bursts. These bubbles coincided with significant economic changes, suggesting a strong link between stock market behavior and economic growth. For instance, financial crises, political instability, and global economic downturns significantly influenced bubble formation and bursts in these markets. The study reveals that market-specific events, such as regulatory changes and shifts in investor sentiment, also contributed to the occurrence of bubbles. Three key policy options are proposed to address bubbles in the studied markets including, enhancing regulatory frameworks to monitor and mitigate bubble formation, improving financial literacy among investors to promote informed decision-making, and strengthening economic policies to stabilize macroeconomic conditions and reduce vulnerability to external shocks. By implementing these measures, policymakers can enhance market stability and foster sustainable economic growth in African stock markets. Full article

This study aims at examining whether hedging emerging Eastern Europe stock markets with commodities sectors can help in reducing market risks and whether it has the same effectiveness among different sectors. As an attempt to achieve this goal, we opt for three types of MGARCH model. These are DCC, ADCC and GO-GARCH, which are used with each bivariate series to model dynamic conditional correlations, optimal hedge ratios and hedging effectiveness. Rolling window analysis is used for out-of-sample one-step-ahead forecasts from December 1994 to June 2022. The results have shown that the commodities sectors of industrial metals and energy represent the optimal hedging instruments for emerging Eastern Europe stock markets as they have the highest hedging effectiveness. Additionally, our empirical results have proved that hedge ratios estimated by the DCC and ADCC models are very similar, which is not the case for GO-GARCH, and that hedging effectiveness is preferably estimated by the ADCC model. Full article

This study employs mainly the Bayesian DCC-MGARCH model and frequency connectedness methods to respectively examine the dynamic correlation and volatility spillover among the green bond, clean energy, and fossil fuel markets using daily data from 30 June 2014 to 18 October 2021. Three findings arose from our results: First, the green bond market has a weak negative correlation with the fossil fuel (WTI oil, Brent oil, natural gas, heating oil, and gasoline) and clean energy markets, which means that green bonds play a critical hedging role against fossil fuel and clean energy. Second, the green bond and clean energy are net volatility receivers from WTI crude oil and heating oil for the short term, indicating that investors and policymakers need to pay attention to the WTI oil volatility spillover risk when promoting green bonds and clean energy. Third, the correlation and volatility spillover from WTI crude oil to green bonds and clean energy is stronger than that of Brent oil, which implies that investors and policymakers need to consider the price movements of WTI crude oil more than Brent oil when investing in the green bond market. In summary, our conclusion is that investors should be aware that green bond investing addresses the two-pronged investment strategy of (i) risk diversification and (ii) carbon mitigation. Thus, this study can provide essential information for energy investors and policymakers to achieve sustainable investment. Full article

Considering the growing importance of sustainable investments worldwide, we explore the volatility transmission effects between the EURO STOXX Sustainability Index and the stock market indexes of its stocks. Using daily index return data, during 2000–2022, covering the COVID-19 pandemic, Multivariate Generalized Auto-Regressive Conditional Heteroskedasticity (MGARCH) models are used to explore if volatility effects of the stock indices felt during the pandemic implied any evolution in the effects already felt between the volatilities existing in these stock indices and the effects of stock market indices’ volatility over the sustainability index. Results point to the great dependence that the sustainability index has on stock index movements. The volatility felt in stock indices during the pandemic period did not become decisive in reversing a previous correlation trajectory between the stock market and sustainability indexes. Provided that sustainability is not observed exclusively in financial and economic terms, but in a triple bottom line context (including the social and environmental sides), we should not verify a high influence of stock market indexes over the sustainability index, as the results point out. Policymakers and investors should be aware of the high influence and take measures to turn the sustainability index more independent. Full article

Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC–MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic are higher than those during the 2008 financial crisis owing to the further opening of the Chinese capital market, and financial institutions’ investments in the European market are higher than those in the American markets; (3) a stepwise increase is observed in the dynamic conditional correlation between the returns on the S&P 500 Index and SSEC during and after the onset of a destructive crisis; and (4) a unidirectional contagion effect exists between the Chinese market and US market, and the Hong Kong stock market contributes to the risk spillover. Effective transmission channels of external negative shocks may be investors’ sentiments, financial institutions, and the RMB exchange rate in the stock markets. This study provides useful suggestions to authorities formulating financial regulations and investors diversifying risk investments. Full article

Crude oil and natural gas are crucial to the Russian economy. Therefore, this study examined the interconnections between crude oil price, natural gas price, and Russian economic policy uncertainty (EPU) over the period 1994–2019 using multivariate DCC-MGARCH models. The findings show that there are strong interconnections (co-movement) between the energy prices and EPU in Russia, and that it might be misleading to assume independence or neutrality between the variables. Although Russia is also a crucial player in both the natural gas and the crude oil markets, this study reveals that there is a stronger co-movement of the EPU with gas price than with the oil price. Russia is the largest exporter of natural gas and the second-largest producer; it is plausible that the natural gas price correlates with EPU more than the crude oil price. Further, the correlation between gas price and EPU and the correlation between crude oil price and EPU have similar patterns. Each declines almost in the same period and, equally, increases concurrently. In addition, the results revealed that significant global shocks and crises, such as the 2008 global financial crisis, the 2014–2017 Russian financial crisis, the 9/11 terrorist attack, and the Russo–Ukrainian conflicts, influence the interconnections between the energy prices and Russian EPU. Full article

There are diverging results in the literature on whether engaging in ESG related activities increases or decreases the financial and systemic risks of firms. In this study, we explore whether maintaining higher ESG ratings reduces the systemic risks of firms in a stock market context. For this purpose we analyse the systemic risk indicators of the constituent stocks of S&P Europe 350 for the period of January 2016–September 2020, which also partly covers the COVID-19 period. We apply a VAR-MGARCH model to extract the volatilities and correlations of the return shocks of these stocks. Then, we obtain the systemic risk indicators by applying a principle components approach to the estimated volatilities and correlations. Our focus is on the impact of ESG ratings on systemic risk indicators, while we consider network centralities, volatilities and financial performance ratios as control variables. We use fixed effects and OLS methods for our regressions. Our results indicate that (1) the volatility of a stock’s returns and its centrality measures in the stock network are the main sources contributing to the systemic risk measure, (2) firms with higher ESG ratings face up to 7.3% less systemic risk contribution and exposure compared to firms with lower ESG ratings and (3) COVID-19 augmented the partial effects of volatility, centrality measures and some financial performance ratios. When considering only the COVID-19 period, we find that social and governance factors have statistically significant impacts on systemic risk. Full article

This study analyzes the volatility spillover effects in the US stock market (S&P500) and cryptocurrency market (BGCI) using intraday data during the COVID-19 pandemic. As the potential drivers of portfolio diversification, we measure the asymmetric volatility transmission on both markets. We apply MGARCH-BEKK and the algorithm-based $G{A}^{2}M$ machine learning model. The negative shocks to returns impact the S&P500 and the cryptocurrency market more than the positive shocks on both markets. This study also indicates evidence of unidirectional cross-market asymmetric volatility transmission from the cryptocurrency market to the S&P500 during the COVID-19 pandemic. The research findings show the potential benefit of portfolio diversification between the S&P500 and BGCI. Full article

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term. Full article

The cryptocurrency market has experienced stunning growth, with market value exceeding USD 1.5 trillion. We use a DCC-MGARCH model to examine the return and volatility spillovers across three distinct classes of cryptocurrencies: coins, tokens, and stablecoins. Our results demonstrate that conditional correlations are time-varying, peaking during the COVID-19 pandemic sell-off of March 2020, and that both ARCH and GARCH effects play an important role in determining conditional volatility among cryptocurrencies. We find a bi-directional relationship for returns and long-term (GARCH) spillovers between BTC and ETH, but only a unidirectional short-term (ARCH) spillover effect from BTC to ETH. We also find spillovers from BTC and ETH to USDT, but no influence running in the other direction. Our results suggest that USDT does not currently play an important role in volatility transmission across cryptocurrency markets. We also demonstrate applications of our results to hedging and optimal portfolio construction. Full article

Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoin’s cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold. Full article

Economic policy uncertainty and particularly COVID-19 has stimulated the need to investigate alternative avenues for policy risk management. In this context, this study examines the dynamic association among economic policy uncertainty, green bonds, clean energy stocks, and global rare earth elements. A dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model was used to gauge the time-varying co-movements among these indices. The analysis finds that green bonds act more as a hedge than a safe haven against economic policy uncertainty (EPU). In the case of diversification, green bonds work as diversifiers with clean energy stocks and rare earth elements during COVID-19 and in the whole sample period. Additionally, clean energy stocks and rare earth elements show safe haven properties against EPUs. This study contributes to the hedging and safe haven literature with some new insight considering the role of green bonds and clean energy stocks. Additionally, the outcomes of the research contribute toward the literature of portfolio diversification theory. These findings pave the way for not only US investors to hedge long-term economic policy risk by investing in green bonds, but also for China and the UK, as these financial assets (green bonds, clean energy stocks, and rare earth metals) and EPU are long-term financial and economic variables. Full article