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This study measures the immediate impact of Russia–Ukraine war on the global stock markets for the first four months since Russia's first invasion attempt on February 24, 2022. Daily closing stock indices have been used from selected stock markets of six different continents. By applying event study method, it observes mixed impact on different stock markets. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH 1,1) indicates the presence of significant volatility and leverage effect in all the markets. Regression estimates show significantly positive impact of VIX and negative impact of oil on the abnormal returns of the global stock markets. Diversifying energy supply and source, accelerating deployment of renewables and promoting electronic vehicles and machines might bring positive result for the financial market. It is expected that this research will provide policymakers, regulatory authorities, investors and all concerned stakeholders a precise guideline to handle the immediate impact of war on the stock prices and to formulate appropriate strategies to keep investment free from risk and uncertainties. [ABSTRACT FROM AUTHOR]

The paper investigates the intricate relationship between oil and stock markets in the context of different oil price shocks and a country's energy profile. Focusing on five major oil-producing and consuming countries, the analysis reveals a significant, time-varying dimension in the oil-stock relationship. It highlights the importance of distinguishing between various types of oil shocks and a nation's reliance on oil revenues. The paper finds that countries heavily reliant on oil revenues exhibit robust responses to oil supply and demand shocks. In contrast, oil producers show more moderate reactions due to lower oil revenue dependence. This evidences the importance of recognizing the multifaceted impacts of oil shocks on different economies for making well-informed decisions in asset allocation, portfolio management, and policymaking. [ABSTRACT FROM AUTHOR]

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph, Dynamic Minimal Spanning Tree and Dynamic Threshold Networks. Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40 % improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk. [ABSTRACT FROM AUTHOR]

The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in extreme volatility in stock markets. This study mainly examines the predictive ability of the Internet concern about COVID-19 on stock index returns, based on the framework of GARCH type models. Instead of using the whole sample period, we divide the Internet concern about COVID-19 into high-concern and low-concern periods by breakpoint test method and then examine its predictive ability for stock returns in different periods, respectively. Using stock indexes of 10 countries and abnormal Google search volume of 'coronavirus' as study samples, the results reveal that (1) the Internet concern about COVID-19 has a negative impact on the stock index returns in the whole and high-concern periods, while its influence in the low-concern period is mixed; (2) the Internet concern about COVID-19 improves the prediction accuracy of stock index returns in the high-concern period, while seems to lose its powerful predictive ability in the whole and low-concern periods. [ABSTRACT FROM AUTHOR]

Countries are experiencing a surge in political, economic, and financial integration, consequently shaping international market linkages. Financial crises can rapidly spread between countries, emphasizing the need to monitor and assess stock market connections. This paper investigates the degree of financial market connectedness using daily stock returns from January 1997 to August 2017 for 13 countries, both developed and developing. The connectedness measure of Diebold and Yilmaz (2009-2012) is applied to examine the connectedness of stock market returns and the direction of spillovers for all stock markets. This study also analyzes the dynamic connectedness from the U.S. stock market to all other stock markets. The results indicate that the U.S. stock market is the most influential stock market to the others. The results of the dynamic analysis show that connectedness changes over time, specifically during turmoil periods. Most developed countries are transmitters of return spillover shocks while developing countries are recipients. [ABSTRACT FROM AUTHOR]

This paper investigates the multiscale non-linear risk transmission effect from Brent oil to eleven European emerging stock markets. Dynamic extreme risk time series are created using the FIAPARCH-CVaR approach. The MODWT transformation is applied to make three wavelet details that represent different time horizons. In the final step, the MODWT time series are fitted into the Markov switching model to examine the spillover phenomenon. The results indicate that the Czech and Hungarian stock markets endure the spillover effect in crisis regime in the short term, probably because these markets are among the most efficient emerging European markets. On the other hand, a relatively high spillover effect is found in a peaceful rather than a crisis regime in the case of Poland. This is probably because the Polish index lists almost 300 stocks, which means that oil shocks disperse to a large number of different industry sectors. In small and less developed markets, such as Estonia, Slovenia, Bulgaria, and Croatia, a high spillover effect exists in a tranquil regime because these countries have high oil consumption per capita. Lithuania and Latvia do not report the spillover effect in the short run, while this is true for all time horizons in the case of Slovakia. [ABSTRACT FROM AUTHOR]

This paper examines the market reaction to the European bank stress test announcement and results release events. Using event study methodology (calculating abnormal returns on a three-day period around the event dates), we find that the market reacts differently between the announcement event and the results release event. We also show that the market seems to positively overreact one day before each event, and that this positive reaction is either fully or partially reversed one day after the event. We thus conclude that researchers should consider both events when exploring the market reaction to stress-testing exercises. [ABSTRACT FROM AUTHOR]

In this paper, we examine the issue of sustainability in the stock markets by comparing various statistical properties of the classical stock market indices against the recent sustainable ones. Weekly and monthly data from Dow Jones, Eurostoxx and Hang Seng indices were collected, and fractional integration methods were used to analyze differences in terms of persistence and mean reversion for both sustainable and common indices. The results indicate high levels of persistence in all cases, observing almost no differences across the markets. Long memory is also detected in the absolute and squared returns in both markets. [ABSTRACT FROM AUTHOR]

We identify start days of 156 episodes of social unrest from textual analysis of media reports and show a systematic negative impact of social unrest on stock market performance. Social unrest on average leads to a 1.4 percentage point drop in cumulative abnormal returns in 2 weeks, more for events that last longer and that happen in emerging markets. Stronger institutions, particularly better governance and more democratic systems, are associated with a smaller adverse impact of social unrest on stock market returns. We argue this reflects the ability of better institutions to provide a more reliable way to reconcile conflicting views and dampen uncertainty after unrest. [ABSTRACT FROM AUTHOR]

Purpose – Despite the growing recognition of the complex interplay between macroeconomic shock indexes and stock market dynamics, there is a significant research gap concerning their interconnectedness and return spillovers in the context of the African stock market. This leaves much to be desired, given that the financial market in Africa is arguably one of the most preferred destinations for hedge and portfolio diversification (Alagidede, 2008; Anyikwa and Le Roux, 2020). Further, like other financial markets across the globe, the increased capital flow, coupled with declining information asymmetry in Africa, has deepened intra and inter-sectoral integration within and across national borders. This has, thus, increased the susceptibility of financial markets in Africa to spillover of shocks from other sectors and jurisdictions. Additionally, while previous studies have investigated these factors individually (Asafo-Adjei et al., 2020), with much emphasis on developed markets, an all-encompassing examination of spillovers and the connectedness between the aforementioned macroeconomic shock indexes and stock market returns remains largely unexplored. This study happens to be the first to consider the impact of each of the indexes on stock returns in Africa, with evidence spanning from May 2007 to April 2023, covering notable global crisis episodes such as the Global Financial Crisis (GFC), the COVID-19 pandemic and the Russia–Ukraine war. Design/methodology/approach – This study employs the novel quantile vector autoregression (QVAR) model, making it the first of its kind in literature. By applying the QVAR, the study captures the potential nonlinear and asymmetric relationship between stock returns and the factors of interest across different quantiles, i.e. bearish, normal and bullish market conditions. Thus, the approach allows for a more accurate and nuanced examination of the tail dependence and extreme events, providing insights into the behaviour of the variables under extreme events. Findings – The study revealed that connectedness and spillovers intensified under bearish and bullish market conditions. It was also observed that, among the macroeconomic shock indicators, FSI exerted the highest influence on stock returns in Africa in both bullish and normal market conditions. Across the various market regimes, the Egyptian Exchange (EGX) and the Nairobi Stock Exchange (NSE) were net receiver of shocks. Originality/value – This study happens to be the first to consider the impact of each of the indexes on stock returns in Africa, with evidence spanning from May 2007 to April 2023, covering notable global crisis episodes such as the GFC, the COVID-19 pandemic and the Russia–Ukraine war. On the methodology front, this study employs the novel QVAR model, making it one of the few studies in recent literature to apply the said method.

The global outbreak of COVID-19 in 2020 became unprecedented and sent shockwaves through financial markets worldwide. This study investigates the impact of the pandemic on the performance of the U.S. financial market from March 1, 2020, to April 14, 2022. Utilizing Bai and Perron’s (1998) least squares with breaks during this period. The study’s test findings validate the existence of seven structural changes, signifying the occurrence of eight effects of independent factors on the S&P 500 index. The empirical findings demonstrate a substantial influence of COVID-19 pandemic on the performance of financial markets. Specifically, the impact of the number of COVID-19 cases and new fatalities on financial market performance, exhibits variations in terms of direction, nature, depth, and level. Based on an analysis of the structural changes, it can be inferred that the initial period exhibits the most pronounced negative impact on the number of new COVID-19 cases. Subsequently, the direction and nature of this impact undergo a transformation from the second to the eighth periods. Specifically, the impact of the number of new COVID-19 cases becomes positive in the second, third, sixth, seventh, and eighth periods, gradually diminishing until it reaches its lowest levels of impact in the eighth period. The research further identifies a detrimental impact on the occurrence of new fatalities. However, the periods spanning from the third to the fifth period exhibit very modest levels of influence, which then transition into a beneficial effect during the fifth period. Moreover, the research reveals that the impact of mortality rates on the performance of the United States stock market was greater than that of COVID-19 cases across all periods linked to structural changes. Additionally, the exchange rate of the dollar has a consistent and favorable impact, and the real interest rate has a pronounced negative impact, which gradually reduces over time and eventually transitions into a positive value by the eighth period.

An increasing number of Chinese companies are strengthening their commitment to corporate social responsibility (CSR) by reporting their carbon emission reductions. Research suggests that such voluntary environmental disclosures can reduce information asymmetry, decrease the cost of capital, and thus enhance firm value. However, a significant research gap exists because this evidence mainly derives from developed countries, with limited consideration given to emerging countries, where the capital market often lacks robust informational content. To fill this research gap, we endeavor to explore the firm-value effects of the disclosures and the magnitude of carbon reduction in China, the largest emerging market. By manually collecting data on carbon emission reductions from the CSR reports of Chinese listed companies from 2008 to 2013, we employ the balance sheet valuation model and ordinary least squares regression to investigate the firm-value effects of the disclosures and the magnitude of carbon emission reductions. The empirical results show that firm value is positively associated with not only voluntary carbon emission reduction disclosures, but also the magnitude of carbon emission reductions. In our sample, for every additional ton of carbon emission reduction, firm value increases by approximately 340 yuan (equivalent to 44 euros or 52 US dollars). We also find that the firm-value effects of carbon emission reductions are more pronounced in high-pollution industries than those in low-pollution industries. Further channel tests reveal that on average, agency problems, as measured by selling, general, and administrative expenses, are lower in firms that disclose carbon emission reduction information than those that do not. In addition, firms with carbon emission reduction disclosures can obtain more green subsidies than those without such disclosures. To mitigate climate risk, firms should proactively engage in carbon emission reductions, which can potentially receive a positive recognition from the capital market. Our study also provides valuable insights for regulators in terms of establishing and enhancing carbon accounting standards to encourage corporate involvement in carbon reductions. JEL Classifications: Q54, Q56, G12 Keywords: carbon emission reductions, firm value, voluntary disclosures, high-pollution industries, INTRODUCTION Global climate change poses threats to ecosystems and could potentially induce unprecedented adverse effects on the global economy and society. Thus, numerous international efforts have concentrated on reducing greenhouse [...]

Climate risk represents an increasingly vital issue to countries, companies, and institutional investors, making it a reality but not a distant threat to humanity. Considering the effects of climate risks on firms' financial indices and financing options, the study investigates whether climate risk is priced by the capital markets of South Africa. The study used reported carbon emissions as a measure of climate risk of 81 listed companies in the Johannesburg Stock Exchange from 2011 to 2020 to examine whether climate risk is considered and priced by the South Africa capital market. Data was sourced from DataStream database- a global financial and macroeconomic time-series database providing data on equities, stock market indices, currencies, company fundamentals, fixed income securities, and key economic indicators. We used the two-step system Generalized Method of Moments estimation technique that copes with endogeneity concerns to corroborate the effects of climate risk on cost of capital and capital structure. We find that climate risk is priced in both cost of debt capital and cost of equity capital. Specifically, we find that an increase in a firm's exposure to climate risk increases the cost associated with issuing debt and equity capital. We also find that climate risk exposure decreases the debt-equity ratio. Additionally, the study showed that firm size, leverage ratio, capitalization, profitability, and turnover affect both cost of capital and capital structure of listed firms. The study concludes that climate risk is priced in cost of financing in the capital market of South Africa. The study recommends that firms should invest in installing eco-friendly machinery that aligns with changing market expectations in order to reduce their carbon emissions. The study therefore highlights the need for companies to proactively assess and manage climate risks, incorporate climate considerations into their strategic decision-making, and enhance their resilience to climate-related challenges. JEL Classifications: C23; G10, Q50 Keywords: Climate risk, cost of capital, capital structure, capital markets, INTRODUCTION Climate risk represents an increasingly vital issue to countries, companies, and institutional investors (Ginglinger, 2020), making it a reality but not a distant threat to humanity (Hjort, 2016). Since [...]

'The ability to deconstruct and analyze and criticize what's being said about the economy and the deficit and inflation... would certainly be something that would mean a lot today.' --Lance […]

Abstract The study applies the wavelet local multiple correlations to investigate the level of comovements among the tail risks of US and emerging Asian stock markets in both time and frequency domains. Through this empirical investigation, we address the question of how the transmission of tail risk across the concerned stock markets is changing over specific timescales, varying from short term to long term. Empirical results from the multivariate time–frequency correlations show that the comovements of tail risks are distinctively higher during periods of economic and political turmoil in the short term. The multivariate long-term comovements are highly stable and extremely strong which can be taken as evidence of long-term integration. In contrast, the bivariate time–frequency correlations are remarkably weaker in the short term not only during periods of crises but over most of the sample period. The results of the bivariate analysis also highlight the instability of the long-term pairwise correlations of the tail risks, showing that it is susceptible to sudden changes, which indicates that the tail risks of the US and emerging Asian stock markets are actually not completely integrated in the long term. This finding also implies that the tail risks of US and emerging Asian stock markets are nonlinearly connected in the long term.

Weak-form efficiency means that stock prices should reflect all historical information and follow a random walk. This study examines the effect of the COVID-19 pandemic on the stock market weak-form efficiency of Arab countries, namely, Jordan, Lebanon, Kuwait, Morocco, Oman, Palestine, Bahrain, Egypt, Iraq, Qatar, Saudi Arabia, the United Arab Emirates, Syria, Tunisia, and Sudan. Daily data from July 1st, 2021 to November 12th, 2022 (370 trading days) are used to cover the period after starting the pandemic. The variance ratio and the runs test are used to test return predictability. The results show that the variance ratio values of Boursa Kuwait, the Egyptian Exchange, Tadawul, and the Amman Stock Exchange are statistically significant, indicating that their returns are unpredictable. In specific, the indices of these stock markets follow a random walk, and their price changes are independent. This is evidence that these stock markets are efficient at a weak level. In contrast, the insignificant values of the variance ratio indicate that returns are predictable in other Arab stock exchanges after the pandemic era. The findings of the Egyptian Exchange, Tadawul, and the Amman Stock Exchange are confirmed using the run test of weak-form efficiency. It reveals that the indices of these stock exchanges follow a random walk, while the indices of other Arab stock markets do not.

Investments in green energy can help optimize investment portfolios and ‘hedge’ against volatile markets, according to a new study. Investors try to avoid financial losses by diversifying their portfolios with [...]

The study applies the wavelet local multiple correlations to investigate the level of comovements among the tail risks of US and emerging Asian stock markets in both time and frequency domains. Through this empirical investigation, we address the question of how the transmission of tail risk across the concerned stock markets is changing over specific timescales, varying from short term to long term. Empirical results from the multivariate time–frequency correlations show that the comovements of tail risks are distinctively higher during periods of economic and political turmoil in the short term. The multivariate long-term comovements are highly stable and extremely strong which can be taken as evidence of long-term integration. In contrast, the bivariate time–frequency correlations are remarkably weaker in the short term not only during periods of crises but over most of the sample period. The results of the bivariate analysis also highlight the instability of the long-term pairwise correlations of the tail risks, showing that it is susceptible to sudden changes, which indicates that the tail risks of the US and emerging Asian stock markets are actually not completely integrated in the long term. This finding also implies that the tail risks of US and emerging Asian stock markets are nonlinearly connected in the long term. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature on carry trade by investigating the dynamic correlation and the dependence structure between the US-dollar carry trade and equity markets in the (Brazil, Russia, India, China and South Africa (BRICS)) economies during sample observations that include regular and crisis periods. Furthermore, the nonlinear Granger causality test based on the feed-forward neural networks (FFNN) model assesses how global volatility predicts the dynamic correlation between the US-dollar carry trade and equity markets in BRICS. The paper finds the dynamic correlations between carry trade and equity markets in BRICS are more pronounced during most global crises. Moreover, the results of the symmetrised Joe Clayton (SJC) copula model showed that the lower tail dependence between the two series is higher during the various crises. Furthermore, the results of the empirical analysis show that global volatility predicts the dynamic correlations between carry trade and equity markets in BRICS only during crises. Asset managers and investors can benefit from this paper's findings regarding portfolio diversification, risk management, asset allocation, and hedging when dealing with equity assets and carry trades. [ABSTRACT FROM AUTHOR]

The efficient market hypothesis assumes that financial markets fully incorporate all available information, rendering past information irrelevant for predicting future prices. However, numerous studies challenge this notion and suggest the presence of long-term memory in market dynamics. Understanding long-term memory in financial markets has important implications for investors and policymakers. The aim of this study was to empirically investigate long term memory in financial markets. This study employed a Hurst model for a sample of 5 financial markets from June 1, 2018, to June 1, 2023. The findings revealed that four out of the five sampled financial market exhibits long term memory which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjust portfolio weights to take advantage of potential return predictability and manage risk. [ABSTRACT FROM AUTHOR]

This article employs an Empirical Mode Decomposition (EMD)-based multiresolution causality approach to explore the scale-by-scale interconnectedness between Bitcoin and the stock markets of Gulf Cooperation Council (GCC) countries. EMD is utilized to decompose signals into intrinsic mode functions (IMFs), which delineate variations across different frequency scales, thus facilitating the identification of distinct oscillations and trends within the signals. The study reveals a significant positive connectedness between Bitcoin and most GCC stock markets, albeit with some variations observed across countries and time periods. Employing multiresolutional causal analysis through EMD provides a valuable framework for examining the nonlinear relationships among financial assets, offering insights into Bitcoin's potential as a diversification tool in certain periods within GCC stock markets. • A novel EMD-based multiresolutional causality paradigm is introduced. • This tool is employed to study the multiscale connectedness bitcoin-GCC stock markets. • We identify significant links between most GCC stock markets and Bitcoin • We highlight Bitcoin's role as an expanding variable in GCC stock market forecasting. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the COVID-19 pandemic on CDS, stock returns, and economic activity in the US and the five European countries that have been most affected: the UK, Germany, France, Italy, and Spain. The sample period covers the period from 11 March 2020 to 19 February 2021. In the empirical analysis, first, we estimate benchmark linear VAR models and then, given the evidence of parameter instability, TVP-VAR models with stochastic volatility, which are ideally suited to capturing the changing dynamics in both financial markets and the real economy. The linear VAR responses of CDS to the number of COVID-19 cases are positive and statistically significant, whilst those of electricity consumption are insignificant and those of stock returns vary across countries in terms of their sign and significance. The results from the TVP-VAR analysis indicate that the effects of shocks on the system variables was more pronounced during the initial stages of the pandemic and then decreased in the following months. Specifically, there was a positive impact of the number of COVID-19 cases on CDS and a negative one on stock returns and economic activity, the latter two being interlinked. [ABSTRACT FROM AUTHOR]

Stock markets have always provided countries with a practical and flexible way to finance their economies. Hence, Islamic finance has embraced the stock market since the early 90s adopting the same framework as an ethical investment. Accordingly, Islamic investors in emerging countries shall have a range of choices when constructing a financial portfolio. However, existing screening methodologies lack flexibility as they are mainly based on rigid ratios and irrelevant thresholds. Consequently, these methodologies lead to an inefficient stock index as they completely ignore the features of each stock market along with the specificities of each industry. Thus, our study will try to propose a new screening methodology based on the optimal financial structure of each industry. The main objective of our study is to propose a methodology that will overcome different loopholes addressed in the literature. The present paper is an explanatory study which needs an empirical confirmation of the proposed methodology in order to measure its performance and efficiency against existing shari’ah-compliant indices. Hence, the main preliminary finding of our research is to enrich the academic debate on shari’ah-compliant screening methodologies through appealing to conventional corporate finance framework to enhance current methodologies. [ABSTRACT FROM AUTHOR]

The pandemic caused by the novel coronavirus COVID‐19 has impact the economies of countries across the world. In a short period of time, researchers have begun to analyse the effect of the pandemic on global stock markets. Although the most known measurements of COVID‐19 are the number of new cases and deaths, there are more robust indicators. In particular, the effective reproductive number is one of the most important indicators to analyse the pandemic which indicates the degree to which the spread is under control. In this paper, we assess the impact that the Effective Reproductive Number (Rt) has on 26 countries around the world (32 stock market indexes) comparing the performance of various forms of Generalized AutoRegressive Conditional Heteroskedasticity models. The results demonstrate that of the 32 stock markets analysed, 37.5% had a negative effect with respect to Rt and only in 12.5% of the cases was the effect of the variation of Rt positive. This implies that in more than a third of the stock markets analysed as the pandemic progressed uncontrolled the result was a decrease in the value of the market index. The 11 of the 26 countries analysed had a negative and significant effect (Brazil, Germany, Indonesia, Israel, Italy, Japan, Russia, South Korea, Sweden, Taiwan, and United States). Findings suggest that the Effective Reproductive Number volatility had a significant impact on 10 of the 26 countries analysed (38.5%) (Australia, Brazil, Canada, China, India, Italy, Mexico, Russia, Singapore and United Kingdom). [ABSTRACT FROM AUTHOR]

The study of macroeconomic forces and the stock market is a dynamic field in finance because investors' interest changes due to their preference for high returns over lower returns and risk-averse investors are always desirous of mitigating risk across emerging economies are playing a pivotal role in the global investment communities. Therefore, this study focuses on various economic indicators that robust business growth. The primary purpose of this study is to evaluate macroeconomic forces' linkages with stock markets by applying a panel data analysis. Data was taken for the period 1999 to 2019 for Asian stock markets. Panel data econometric techniques have been used to analyze the data of the Asian equity market. Studies have taken stock market data from five emerging economies, i.e., Pakistan, China, India, Philippines, and Japan. Panel data results confirm that gross domestic product, interest rates, exchange rate, and money supply positively impact market return. However, correlation results reveal that market return strongly relates to foreign direct investment, interest rate, and money supply. The co-integration result also reveals the presence of a long-run relationship. Further results show that GDP has a leading behavior with market return, and market return has a leading behavior with GDP, CPI, ER, and IPI. [ABSTRACT FROM AUTHOR]

This article employs quantile regressions to investigate the link between oil prices, gold prices, and stock market returns in five African oil-exporting nations: Nigeria, Ghana, Egypt, Algeria, and Tunisia. The analysis utilises daily data spanning from January 1, 2017, through September 30, 2021. Before the onset of COVID-19, oil played a pivotal role as a hedge in Tunisia, Egypt, Algeria, and Ghana, providing stability in both normal and bullish market conditions. Furthermore, it functioned as a diversifier in Egypt and Ghana during bearish market phases. However, as the pandemic unfolded, oil took on the characteristics of a safe haven in Egypt, Ghana, Nigeria, Algeria, and Tunisia, particularly notable during bearish and normal market conditions. Gold, prior to the pandemic, served as a hedge in Egypt, Ghana, Nigeria, and Tunisia, while also acting as a diversifier across both bearish and bullish market conditions in these economies. Conversely, during the pandemic, gold predominantly functioned as a diversifier in most oil-exporting African economies, with significant hedge properties observed specifically in Algeria and Ghana. The practical implications of these findings are significant for various stakeholders, including oil exploration and production firms, businesses in the transport and hospitality sectors, policymakers, and investors with stocks sensitive to oil and gold prices.

Purpose – This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers. Design/methodology/approach – The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation. Findings – Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors. Originality/value – The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

Abstract The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.

Abstract The enduring impact of the COVID-19 crisis on the financial sector is undeniable, persisting far beyond the eventual waning of the pandemic. This research examines central bank interventions during the pandemic, using a quantitative event study approach over a five-day window to analyse the impact of 188 monetary policy announcements on banking stocks in China, the U.S., and Europe. Our results demonstrate how monetary policy announcements targeting different economic mechanisms have produced a diverse market reaction throughout the COVID-19 pandemic. Namely, cuts in interest rates and the maintenance of a low interest rate environment by the Federal Reserve resulted in negative abnormal returns in the U.S.A., while short-term announcements surrounding intra-day credit and liquidity provisions boosted banking sector stock prices. In Europe, a muted reaction by the banking sector was observed, with negative abnormal returns observed in response to the ECB’s 2% inflation objectives. Finally, banking stocks in China responded strongly and positively to foreign currency and exchange-related announcements by the People’s Bank of China. The results and insights from this analysis can thus inform preparations made by policymakers, governments, and financial market stakeholders in the event of future waves of COVID-19, or further extreme societal disruptions.