Search Results (14)

This study investigates the cross-country spillover effects of investor sentiment, specifically Fear and Greed, between the United States and South Africa, within the broader context of increasing global financial integration and behavioral finance. Using monthly data from June 2007 to June 2024, this research constructs and tests the validity of a South African Fear and Greed Index, adapted from CNN’s U.S.-centric index, to better capture the unique dynamics and contribute to an alternate sentiment index for an emerging market. Employing the Diebold and Yilmaz (DY) connectedness framework, this study quantifies both static and dynamic spillover effects via a vector autoregression-based variance decomposition model. The results reveal significant bidirectional sentiment transmission, with the U.S. acting as a dominant net transmitter and South Africa as a net receiver, along with notable cross-country effects closely linked to the global economic trend. Spillover intensity escalates during periods of global economic stress, such as the 2008 financial crisis and the COVID-19 pandemic. The findings highlight that the USA significantly influences South Africa and that the adapted SA Fear and Greed Index better accounts for South African market conditions. Full article

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

This study investigates the salient roles of knowledge spillover and environmental policies on clean technology innovation. Employing a panel vector autoregressive model (PVAR) and connectedness network analysis with a comprehensive longitudinal dataset comprising 100 million patent documents across 26 countries, the study identifies clean technology fields that are most efficient in driving innovation and subsequently quantifies the spillover effects for each field. The impact of public environmental policies and regulations on clean technological innovations is also examined in depth. The results reveal that clean innovation is a complex and nuanced system, with significant knowledge spillovers occurring within and across energy and non-energy-related clean technology fields. The results also show that environmental policies significantly influence clean innovation, with technology support and adoption support policies having the most substantial impact. Furthermore, the results reveal that the impact of market-based policies on clean innovation is weaker than that of non-market-based policies, which is an important consideration for policymakers. The findings hold significance for policymakers in addressing sustainability goals and their implications. Full article

Volatility reflects the degree of variation in a time series, and a measurement of the stock performance in the energy sector can help one understand the pattern of fluctuations within this industry, as well as the factors that influence it. One of these factors could be the COVID-19 pandemic, which led to extreme volatility within the stock market in several economic sectors. It is essential to understand this regime of volatility so that robust financial strategies can be adopted to handle it. This study used stock data from the Yahoo! Finance API and data from the energy-price database from the US Energy Information Administration to conduct a comparative analysis of the volatility in the energy sector in Brazil and in the United States, as well as of the energy prices in California. The volatility in these time series were modeled using GARCH. The stock volatility regimes, both before and after COVID-19, were identified with a Markov switching model; the spillover index between the energy markets in the USA and in Brazil was evaluated with the Diebold–Yilmaz index; and the causality between the energy stock price and the energy prices was measured with the Granger causality test. The findings of this study show that (i) the volatility regime introduced by COVID-19 is still prevalent in Brazil and in the USA, (ii) the changes in the energy market in the US affect the Brazilian market significantly more than the reverse, and (iii) there is a causality relationship between the energy stock markets and the energy prices in California. These results may assist in the achievement of effective regulation and economic planning, while also supporting better market interventions. Also, acknowledging the persistent COVID-19-induced volatility can help with developing strategies for future crisis resilience. Full article

This research utilizes the Diebold and Yilmaz spillover model to examine the correlation between geopolitical events, natural disasters, and oil stock returns in Asian OPEC+ member countries. The study extends prior research by investigating the dynamics of the Asian OPEC+ oil market in light of recent exogenous events. The analysis commences by creating a self-generated Asian OPEC+ index, which demonstrates significant volatility, as indicated by GARCH (1, 1) model estimation. The results obtained from the Diebold and Yilmaz spillover test indicate that, on average, there is a moderate degree of connectedness among the variables. However, in the event of global-level shocks or shocks specifically affecting Asian OPEC+ countries, a heightened level of connectedness is found. Prominent instances of spillover events observed in the volatility analysis conducted during the previous decade include the COVID-19 pandemic, the conflict between Russia and Ukraine, and the Turkey earthquake of 2023. Based on the facts, it is recommended that investors take into account the potential risks linked to regions that are susceptible to natural calamities and geopolitical occurrences while devising their portfolios for oil stocks. The results further highlight the significance of integrating these aspects into investors’ decision-making procedures and stress the need for risk management tactics that consider geopolitical risks and natural disasters in the oil equity market. Full article

The launch of the national carbon emissions trading (CET) market has resulted in a closer relationship between China’s CET market and its electricity market, making it easy for risks to transfer between markets. This paper utilizes data from China’s CET market and electric power companies between 2017 and 2023 to construct the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Barun’ik and Křehl’ik, and a minimum spanning tree model. The comparison is made before and after the launch of the national CET market. Subsequently, this paper examines the market spillover effects, as well as the static and dynamic properties of network structures, considering both the time domain and frequency-domain perspectives. The research findings suggest the following: (1) There is a strong risk spillover effect between China’s CET market and the stock prices of electric power companies; (2) There is asymmetry in the paired spillover effects between carbon trading pilot markets and the national CET market, and differences exist in the impact of risk spillovers from power companies between the two; (3) The results of the MST model indicate that the risk contagion efficiency is higher in the regional CET pilot stage compared to the national CET market launch stage, with significant changes occurring in key nodes before and after the launch of the national CET market; (4) Both the dynamic spillover index and the standardized tree length results demonstrate that crisis events can worsen the risk contagion between markets. Besides offering a theoretical foundation and empirical evidence for the development of China’s CET and electricity markets, the findings of this paper can provide recommendations for financial market participants as well. Full article

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness; however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants. Full article

The paper aims to analyze the contagion effect coming from the developed stock markets of the US and Germany to the emerging CEE stock markets of Romania, Czech Republic, Hungary, and Poland using daily data for the period April 2005–April 2021. The paper also captures the level of integration of these emerging stock markets by analyzing the volatility spillover phenomenon. The quantification of the contagion effect coming from the developed to the emerging stock markets consisted of an empirical analysis based on the DCC-GARCH (Dynamic Conditional Correlation) model. Through this multivariate model, the time-varying conditional correlations were analyzed, both in periods of normal economic development and in times of economic instability, when there was a significant increase in the correlation coefficients between developed and emerging stock market indices. Furthermore, the level of connectedness between these markets has been analyzed using the volatility spillover index developed by Diebold and Yilmaz. The empirical results surprised the high level of integration of the analyzed stock markets in Central and Eastern Europe, with the intensity of volatility transmission between these markets increasing significantly during times of crisis. All stock market indices analyzed show periods during which they transmit net volatility and periods during which they receive net volatility, indicating a bidirectional volatility spillover phenomenon. Mostly, the BET, PX, and WIG indices are net transmitters of volatilities, whereas the BUX index is net recipient, except during the COVID-19 crisis, when it transmitted net volatility to the other three indices. Finally, using a Markov switching-regime VAR approach with two regimes, we explored the contagion effect between emerging CEE and developed stock markets during the COVID-19 pandemic. The empirical results proved a shift around the outbreak of the health crisis, after which the high volatility regime dominates the CEE markets. The contagion effects from developed stock markets to emerging CEE markets significantly increased during the first stage of the health crisis. Full article

To achieve sustainable economic growth, a significant amount of private capital must be invested in green industries. However, risk management in the green industry stock market has drawn much attention recently due to the uncertainty and high risk present in this market. By applying the spillover index model of Diebold and Yilmaz, the frequency-domain spillover approach developed by Baruník and Křehlík, and the dynamic conditional correlation (DCC) model, this paper focuses mainly on the heterogeneity of the volatility spillovers among six green industry equities and other financial assets in China, under various market economy situations. Based on the empirical results obtained in this paper, we find that the green industry stock markets have the least impact on the gold and energy futures markets. Additionally, based on asymmetric analyses, it can be concluded that the green bond market has experienced the smallest shocks from the six green industry stock markets. By utilizing frequency-domain analyses, the energy futures market experiences the least amount of volatility from green stocks. Additionally, the COVID-19 pandemic affects the interconnectedness of markets. Prior to the COVID-19 pandemic, energy futures were the most suitable portfolio instrument for green industry stocks. When the COVID-19 pandemic occurred, however, gold proved to be the most advantageous portfolio asset. The research findings of this paper demonstrate the impact of COVID-19 on the selection of the best investment instruments for green industry stocks, which is beneficial for reducing the investment risk of green financial market participants and increasing the demand for green stock markets, while also providing practical advice for environmentally conscious investors and policymakers. Full article

This paper aims to apply the time-varying Granger causality test (TVGC) and the DY Spillover Index (Diebold and Yilmaz, 2012) to measure the Granger causality and dynamic risk spillover effects of the international crude oil futures market on China’s agricultural commodity futures market from the perspectives of return and volatility spillovers. Empirical evidence relating to the TVGC test suggests the existence of unidirectional Granger causality between crude oil futures and agricultural product futures. This relationship shows a strong time-varying property, in particular for sudden or extreme events such as financial crises and natural disasters. On the other hand, the volatility spillover in crude oil and agricultural product futures markets responds asymmetrically and bidirectionally according to the result of the DY Spillover index, and the periodicity of total volatility spillover correlates closely with the occurrence of global economic events, which indicates that the spillover effect between crude oil and agricultural commodity futures markets will be exacerbated in turbulent financial and economic times. Such findings are expected to help in formulating policy recommendations, portfolio design, and risk-management decisions. Full article

This paper explores the impact of the COVID-19 pandemic on the connectedness of stock indexes in the group of developed and emerging economies known as the ASEAN+3. We derived our empirical findings from the Diebold and Yilmaz (DY12) and Baruník and Křehlík (BK18) spillover methods, using daily data from 10 May 2005 to 24 February 2021. We show that the COVID-19 pandemic has had a bigger impact on the return and volatilities of ASEAN+3 stock markets than previous economic turmoil, such as the 2008 global financial crisis and the 2009–2012 European debt crisis. Using a frequency domain methodology, we find evidence that return spillovers mostly occur in the short-term, while volatility connectedness is more pronounced in the long-term. The Singapore stock market primarily acts the as top transmitter in returns and volatilities, whereas Vietnam has become the top receiver of shocks in returns. We also demonstrate that it is possible to replicate the frequency-domain connectedness measures of BK18 with a DY12 methodology. Using a series decomposed with a wavelet-based approach, we find that the total spillover indices for short-, medium-, and long-term frequencies computed with the DY12 approach are comparable to the within connectedness indices of BK18. Our results have important policy implications for investors, regulators, and policy makers. Full article

Using monthly data from September 2004 to February 2020, this paper investigates the connectedness of the renewable energy, common stock, oil, and technology markets. The time-domain Diebold and Yilmaz spillover index approach is used to analyze the volatility spillover between these four markets. The study’s findings reveal that the oil and clean energy markets have bidirectional volatility spillover. The oil market has been found to be a net receiver of volatility. Furthermore, the study shows that volatility spillover is stronger in extreme positive and negative shock periods than in medium shock periods. Our findings show that, during crisis periods, the volatility spillover index rises, while the total connection reached its lowest point in 2015. Our findings suggest that policymakers should be informed that, as long as oil prices remain low, alternative energy-producing industries will not require specific policies to mitigate their vulnerability to crude oil price shocks. However, large spillover in the tails—particularly in the right tail—indicates vulnerability to extreme events, such as the negative effect of oil price increases. Full article

The connectedness dynamics between large-, mid-, and small-cap stocks is investigated using the forecasted error variance decomposition (FEVD) spillover framework of Diebold and Yilmaz in the time-frequency domain. Total volatility spillover (i.e., connectedness) is elevated between large-, mid-, and small-cap stocks during the study period. This high level of spillover exists in the short run only, and declines gradually in the medium to long run, thus providing opportunities for portfolio diversification (hedging) in multi-cap investing during the medium-to-long run (short run) only. Like total connectedness, a similar pattern of bilateral connectedness is observed between either of the two indices, thus providing a similar opportunity in the short and long runs. The mid-cap index emerges as the major contributor to total volatility in the system, followed by the small- and large-cap indices, during the analyzed period. The volatility spillover is time-varying in both the time and frequency domains. Full article

The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom Bubble, the 2008 Housing Crisis, and the 2015 Chinese Bubble. We employ three main empirical methods; the LPPL model to identify asset bubbles, the DCC-GARCH model to measure volatility clustering, and the Diebold-Yilmaz volatility spillover index to measure the level of financial contagion. We provide robust evidence that during the DotCom bubble there was very limited spillover between the S&P 500, the Shanghai, and the Shenzhen Composite Indexes. However, there was significantly more spillover effects in the two more recent crises, i.e., the Housing crisis and the 2015 Chinese Bubble. Together, these results highlight the fact that as financial markets have become more globalized, there are greater levels of volatility transmission and correspondingly fewer potential benefits from international diversification. Full article