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Analogous to an experienced mariner choosing a safe harbour depending on the wind direction, we hypothesize safe-haven asset(s) are conditional on the cause of the market fall. Using oil markets as a salient case study, we find that traditional safe-haven assets, such as the US dollar and government bonds, act as safe havens only when oil prices fall due to declines in actual or expected demand. On the other hand, stock markets provide safe-haven protection when oil prices fall due to increases in oil supply. Therefore, our results suggest that papers that seek to identify safe-haven assets in response to declines in a given asset's return need to test for the possibility that the identified assets might not be safe-haven assets in all circumstances. [ABSTRACT FROM AUTHOR]

This study aims to examine the reciprocal effects of the Economic Policy Uncertainty (EPU) and the Geopolitical Risks (GPR) on the stock markets (SP) of the G-7 countries. The findings of the study will allow us to answer the following questions: Do risk and uncertainty conditions in other G-7 countries affect their stock markets as much as those in the country itself? Which affects G-7 stock markets more, EPU or GPR? In addition to previous research in the field, this study conducts a comparative analysis of the effects of the EPU and GPR on the SP of G-7 countries. Therefore, we used the linear VAR Granger, Fourier and Fourier Fractional Frequency Granger Causality tests. We found that the EPU indices of the United States, United Kingdom, and Germany had the greatest impact on the stock markets of their respective countries and other G-7 countries, and the conclusion that G-7 stock markets were influenced by economic uncertainties in other member countries was added to the literature. It has also been found that the G-7 stock markets have a broad influence on the EPU index. [ABSTRACT FROM AUTHOR]

This article proposes a novel idea for creating a sentiment-based stock market index forecasting model by amalgamating price and sentiment data hidden in the price pattern itself. The state-of-the-art methodologies used in forecasting stock markets involve gathering sentiment data from external sources like tweets, but the proposed model is unique in the sense it extracts the sentiment information from the price itself, making it more reliable and easier to test and implement. In the proposed system the simple daily time series is converted to an information enriched fuzzy linguistic time series with a unique approach of incorporating information about the sentiment behind the Open High Low Close (OHLC) price formation that took place at every instance of the time series. A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system it into a forecasting system. A number of experiments were conducted using the proposed model on Nifty-50 index values (5 years) and it was found that the Root Mean Squared Error (RMSE) value came around 1.03 and RMSE% came around 1.72% which is quite small compared to number of observations and hence this gives a strong indication that the proposed system has the capability to perform good quality of forecasts. The model is simple and easy to implement with very nominal memory requirements, compared to other type of models. [ABSTRACT FROM AUTHOR]

In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved around the assumption that correlations are mediated in a pairwise manner, whereas, in a system as intricate as this, the interactions need not be limited to pairwise only. Here, we introduce a general methodology using information-theoretic tools to construct a higher-order representation of the stock market data, which we call functional hypergraphs. This framework enables us to examine stock market events by analyzing the following functional hypergraph quantities: Forman–Ricci curvature, von Neumann entropy, and eigenvector centrality. We compare the corresponding quantities of networks and hypergraphs to analyze the evolution of both structures and observe features like robustness towards events like crashes during the course of a time period. [ABSTRACT FROM AUTHOR]

Blockchain technology (BCT) is regarded as one of the most important and disruptive technologies in Industry 4.0. However, no comprehensive study addresses the contributions of BCT adoption (BCA) on some special business functionalities projected as financial variables like BCA integrity, transparency, etc. Therefore, the primary objective of this study was to close this theoretical gap and determine how BCA has contributed to the four business sectors that were selected since FinTech had the greatest potential in these domains. The PRISMA approach, a systematic literature review model, was used in this work to make sure that the greatest number of studies on the topic were accessed. The PRISMA model’s output helped identify relevant publications, and an analysis of these studies served as the foundation for this paper’s findings. The findings reveal that BCA for companies with a disrupting financial technology (FinTech) attitude can help in securing corporate transaction transparency; offer knowledge, same-data, and information sharing; enhance fidelity, integrity, and trust; improve organizational procedures; and prevent fraud with cyber-hacking protection and fraudulence suspension. Moreover, blockchain’s smart contract utilization feature offers ESG and sustainability functionality. This paper’s novelty is the projection to four business sectors of the three-layer research sequence: (i) financial variables operated as BCA functionalities, (ii) issues, risks, limitations, and opportunities associated with the financial variables, and (iii) implications, theoretical contributions, questions, potentiality, and outlook of BCA/FinTech issues. And the ability of managers or practitioners to reference this sequence and make decisions on BCA matters is considered a key contribution. The proposed methodology provides business practitioners with valuable insights to reevaluate their economic challenges and explore the potential of blockchain technology to address them. This study combined a systematic literature review (SLR) with qualitative analysis as part of a hybrid research approach. Quantitative analysis was carried out on all 835 selected papers in the first step, and qualitative analysis was carried out on the top-cited papers that were screened. The current work highlights the key challenges and opportunities in established blockchain implementations and discusses the outlook potentiality of blockchain technology adoption. This study will be useful to managers, practitioners, researchers, and scholars.

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.

Blockchain technology (BCT) is regarded as one of the most important and disruptive technologies in Industry 4.0. However, no comprehensive study addresses the contributions of BCT adoption (BCA) on some special business functionalities projected as financial variables like BCA integrity, transparency, etc. Therefore, the primary objective of this study was to close this theoretical gap and determine how BCA has contributed to the four business sectors that were selected since FinTech had the greatest potential in these domains. The PRISMA approach, a systematic literature review model, was used in this work to make sure that the greatest number of studies on the topic were accessed. The PRISMA model's output helped identify relevant publications, and an analysis of these studies served as the foundation for this paper's findings. The findings reveal that BCA for companies with a disrupting financial technology (FinTech) attitude can help in securing corporate transaction transparency; offer knowledge, same-data, and information sharing; enhance fidelity, integrity, and trust; improve organizational procedures; and prevent fraud with cyber-hacking protection and fraudulence suspension. Moreover, blockchain's smart contract utilization feature offers ESG and sustainability functionality. This paper's novelty is the projection to four business sectors of the three-layer research sequence: (i) financial variables operated as BCA functionalities, (ii) issues, risks, limitations, and opportunities associated with the financial variables, and (iii) implications, theoretical contributions, questions, potentiality, and outlook of BCA/FinTech issues. And the ability of managers or practitioners to reference this sequence and make decisions on BCA matters is considered a key contribution. The proposed methodology provides business practitioners with valuable insights to reevaluate their economic challenges and explore the potential of blockchain technology to address them. This study combined a systematic literature review (SLR) with qualitative analysis as part of a hybrid research approach. Quantitative analysis was carried out on all 835 selected papers in the first step, and qualitative analysis was carried out on the top-cited papers that were screened. The current work highlights the key challenges and opportunities in established blockchain implementations and discusses the outlook potentiality of blockchain technology adoption. This study will be useful to managers, practitioners, researchers, and scholars. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative family of statistical models called the multivariate skew scale-shape mixtures of normal distributions. These models serve as a versatile tool in statistical analysis by efficiently characterizing the skewed and leptokurtic nature commonly observed in multivariate datasets. Their applicability shines in real-world scenarios where data often deviate from standard statistical assumptions due to the presence of outliers. We present an EM-type algorithm designed for maximizing likelihood estimation and evaluate the model's effectiveness through real-world data applications. Through rigorous testing against various datasets, we assess the performance and practicality of the proposed algorithm in real statistical scenarios. The results demonstrate the remarkable performance of this new family of distributions. [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]

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]

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile's stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China's economic slowdown in 2012 and the subprime crisis in 2008. [ABSTRACT FROM AUTHOR]

This study investigated the influence of various drivers on the performance of stock markets in Malawi using a case study approach. Stock markets involve strategic collaborations between small and large companies to achieve their goals. This paper aimed to explore the correlation between stock market performance drivers and the performance of companies in Malawi. A mixed-method approach was employed, incorporating both quantitative and qualitative methods. The quantitative approach analyzed the financial aspects of stock market initiatives through financial reports, such as those from the Malawi Stock Exchange. Meanwhile, the qualitative approach collected data through interviews with key stakeholders. The findings indicated a positive outlook for stock market expansion, provided that awareness campaigns among the general public were intensified. The study also revealed that if banks offered tailor-made loans to individuals and companies seeking to acquire shares, the stock market would experience growth. Drivers of stock market performance significantly enhanced market performance, increased stock prices, and improved customer purchasing power. Despite these benefits, the study identified challenges, such as a lack of knowledge about shares, which hindered stock market growth. Other challenges included inflation, harsh loan conditions, and stock/share infringements, all of which negatively impacted market performance. To address these issues, stock markets implemented awareness and marketing initiatives to combat illiteracy and other barriers, aiming to expand their customer base. However, despite the significant benefits of these initiatives, limited research existed on the impact of stock market performance drivers, particularly on stock-listed companies in Malawi. This gap prompted the researcher to evaluate the influence of metrics such as stock growth and customer acquisition. [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 conducts a comparative analysis on the variability of firm-level abnormal returns in Egypt and the United States (US) amidst three different recent global hazardous events (political, economic, and health). Moreover, it determines the impact of firm-level characteristics in reducing firms' abnormal returns and identifies the most vulnerable industries during crises in both countries. The paper methodology is conducted in two stages on the selected sample for US and Egypt stock markets, DJIA and EGX30 indices, respectively. The first stage is an event study approach to determine the amplitude and direction of the response of the US and Egypt abnormal returns to each of the studied crises. The second stage encompasses cross-sectional regressions to study the impact of firm-level characteristics and vulnerable industries on the highest significant abnormal stock returns for each event in each country. This study has theoretical and practical contributions. To our knowledge, this is the first study to demonstrate the higher abnormal stock returns of the developing stock market, Egypt, compared to the developed stock market, US, in both health and economic crises under all different time intervals except the examined political crisis. Moreover, emerging and developed countries' businesses should provide attentive management of this study's significant firm-level characteristics to strengthen firms' exogenous shock absorbency. Furthermore, this study's identification of the most vulnerable industries in each market paves the way for developing supportive policies for these industries resulting in healthier and safer financial markets amidst crises. [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]

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]

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]

This research examines the co-movement between exchange rates and equity prices in a selection of frontier African markets (Ghana, Mauritius, and Tunisia). The analysis encompasses data from 4 January 2010 to 31 March 2023. Employing advanced econometric techniques, the study investigates the interconnectedness of frontier markets and the direction of volatility spillovers between currency and equity markets. Our findings revel that Ghana, Mauritius, and Tunisia are characterized by strong sensitivity to price variations and high volatility. Moreover, significant volatility transmission and spillover effects are observed across the selected markets. Finally, the analysis finds the presence of non-linear dynamics in both the time and frequency domains. In light of these findings, policymakers and investors should incorporate the potential for abrupt and persistent changes, as well as volatility spillovers, into their decision-making processes when considering investments in the Ghana, Mauritius, and Tunisia markets. This research is anticipated to contribute to the development of more robust investment strategies for managing risk exposure within diversified portfolios.

This work examines the impact of Coronavirus disease (COVID-19) on the stock market of Group of Seven (G7) countries during the first wave of COVID-19 using daily data from March, 1st of 2020 to December, 31st of 2020. Focussing on such period, a Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of first wave of COVID-19 on the stock market performance of these G7 countries by employing a Markov Chain Monte Carlo (MCMC) method. We considered an Autoregressive (AR) model with time-varying parameters and a local linear trend model to know if the stock price of these countries during the period of the first wave of COVID-19 is changing over time. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the AR process itself was also changing over time. The stock market of the USA followed by Japan performed better than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.

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.