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The purpose of this paper is to examine market reactions to private equity placements and intra-industry information spillover effects in the Taiwan Stock Exchange.

The authors first use the market model to compute the abnormal announcement returns. To examine the joint impact of the private investment in public equity (PIPE) purposes and the lead investor industry, the authors regress the issuers’ cumulative abnormal returns (CARs) on the dummy variables of PIPE purposes and the lead investor industry. To study the spillover effects, the authors regress the rivals’ CARs on the issuers’ CARs, PIPE purposes, and the lead investor industry. Finally, the industry Herfindahl index is used as a proxy for the market power of issuers and rivals to examine its impact on the spillover effects.

The authors find that issuing firms experience positive abnormal returns during the announcement period. Issuers enjoy more positive market reactions when the proceeds of offerings are primarily used to establish a long-term strategic alliance or to integrate business and when the lead investor is in the same industry. Furthermore, the authors show that the contagion effect dominates the competitive effect in private equity placements at the aggregate level. At the subsample level, the authors find competitive effect overpowers contagion effect when the purpose of offerings is primarily used to establish a long-term strategic alliance or to integrate business and when the lead investor is in the different industry. Finally, the authors show that rivals with relative lower market power enjoy more positive contagion effects.

First, the analysis documents the simultaneous importance of both the purposes of private offerings and the lead investor’s industry on announcement reactions, which shed new light on the positive abnormal returns during the announcement period. Second, the study adds to the literature on the information spillover effects by analyzing the role played by purposes of offerings and rivals’ market power. This paper provides a more complete picture of the offsetting competitive and contagion effects.

The purpose of this paper is to study the evolution of financial contagion between Eurozone banks, observing the credit default swaps (CDSs) market during the period 2009–2017.

The authors use a dynamic spatial Durbin model that enables to explore the direct and indirect effects over the short and long run and the transmission channels of the contagion.

The results show how contagion emerges through physical and financial market links between banks. This finding implies that a bank can fail because people expect other related financial institutions to fail as well (self-fulfilling crisis). The study provides statistically significant evidence of the presence of credit risk spillovers in CDS markets. The findings show that equity market dynamics of “neighbouring” banks are important factors in risk transmission.

The research provides a new contribution to the analysis of EZ banking risk contagion, studying CDS spread determinants both under a temporal and spatial dimension. Considering the cross-dependence of credit spreads, the study allowed to verify the non-linearity between the probability of default of a debtor and the observed credit spreads (credit spread puzzle). The authors provide information on the transmission mechanism of contagion and, on the effects among the largest banks. In fact, through the study of short- and long-term impacts, direct and indirect, the paper classify banks of systemic importance according to their effect on the financial system.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic is an unprecedented shock to the BRICS (Brazil, Russia, India, China, South Africa) economy and their financial markets have plummeted significantly due to it. This paper adds to the recent literature on contagion due to spillover by uniquely examining the presence of pairwise contagion or volatility transmissions in stock markets returns of India, Brazil, Russia, China and USA prior to and during COVID-19 pandemic period.

In this study, the generalised autoregressive conditional heteroskedasticity (GARCH) by Bollerslev (1986) under diagonal parameterization is used to estimate multivariate GARCH framework also known as BEKK (Baba EngleKraft and Kroner) model on stock market returns of BRIC nations and the US.

The empirical results show that the model captures the volatility spillovers and display statistical significance for own past mean and volatility with both short- and long-run persistence effects. Own volatility spillovers (Heatwave phenomenon) have been found to be highest for the US, China and Brazil compared to Russia and India. The coefficients indicate persistence of volatility for each country in terms of its own past errors. The highest and long-term spillover effect is found between US and Russia. The results recommend that Russia is least vulnerable to outside shocks. Finally after examining the pairwise results, it is suggested that the BRIC countries stock indices have exhibited volatility spillover due to the COVID-19 pandemic.

The study may be extended to include other emerging market economies under a dynamic framework.

Researchers and policymakers may draw useful insights on cross-market interdependencies regarding the spillovers in BRIC countries' stock markets. It also helps design international portfolio diversification strategies and in constructing optimal portfolios during COVID and in a post-COVID world.

COVID-19 has been an improbable event in the history of the world which can have a large impact on the financial economies across the emerging countries. This event can be deemed to be informative enough to measure the co-movements of the equity markets amongst cross-country return series, which has not been investigated so far for BRIC nations.

This study aims to explore the spillover effects of data breaches from a consumer perspective in the e-commerce context. Specifically, we investigate how an online retailer’s data breach affects consumers’ privacy risk perceptions of competing firms, and further how it affects shopping intention for the competitors. We also examine how the privacy risk contagion effect varies depending on the characteristics of competitors and their competitive responses.

We conducted two scenario-based experiments with surveys. To assess the spillover effects and the moderating effects, we employed an analysis of covariance. We also performed bootstrapping-based mediation analyses using the PROCESS macro.

We find evidence for the privacy risk contagion effect and demonstrate that it negatively influences consumers’ shopping intention for a competing firm. We also find that a competitor’s cybersecurity message is effective in avoiding the privacy risk contagion effect and the competitor even benefits from it.

While previous studies have examined the impacts of data breaches on customer perceptions of the breached firm, our study focuses on customer perceptions of the non-breached firms. To the best of the authors’ knowledge, this study is one of the first to provide empirical evidence for the negative spillover effects of a data breach from a consumer perspective. More importantly, this study empirically demonstrates that the non-breached competitor’s competitive response is effective in preventing unintended negative spillover in the context of the data breach.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

Previous research observed that large internal fraud events in the general financial services industry imply negative spillover effects, whereas internal fraud in investment banks can imply significantly positive effects for other banks. This paper aims to shed further light on this contradictory result.

For this purpose, the authors compare the spillover effects of the three largest cases of rogue trader events in investment banks (Company 1, 1995; Company 2, 2008; Company 3, 2011) on the largest competing non-announcing banks and insurance companies in Europe based on an event study.

The results show that while the respective announcing firm suffered significant market value losses that even led to bankruptcy in case of Company 1, spillover effects on other banks and insurers were twofold. In particular, in case of Company 2 and Company 3, spillover effects on other financial firms were significantly positive depending on the event window, indicating a dominating competitive effect, whereas the Company 1 event with its resulting bankruptcy led to significantly negative spillover effects and thus contagion.

The results offer a first indication that the severity of the event in terms of its consequences for the announcing firm is crucial, as internal fraud events have the potential to significantly worsen the market values of other financial services firms, which is in contrast to the typically observed positive effects.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Globally influential Islamic banks from the Middle East and Southeast Asia carry voluminous correspondence banking with banks from China and India, leading to potential spillover effect of contagion among the banks from these regions. This study aims to investigate the Islamic banks systemic risk contagion with major banks from China and India.

Having the option pricing theory in the backdrop, the authors calculated three different distance to risk measurements (default, insolvency and capital). The authors have included top six listed globally influential Islamic banks, top seven Indian banks and top eight Chinese banks based on their net asset value. They then measured the banks’ extreme shocks based on the extreme value theory by using the logistic regression model. These extreme shocks helped the authors to map the spillover among the selected banks from multiple regions.

The authors have found strong evidences of directional risk spillover among the banks in this sample. Islamic banks are receiving a significant risk spillover from the other sample banks but transmitting less toward the other banks from India and China. Hence, there is strong one-directional risk contagion toward the Islamic banks in the study sample.

This research would be particularly useful to the regulators and bankers from emerging and Islamic markets to understand the conniving nature of the crisis by effectively mapping the source, destination and implementation of the shock transmission mechanism of the potential financial contagion.

Even though the corresponding banking among the top Islamic banks from the Middle East and Southeast Asian countries, and banks from India and China, is on the rise, the assessment of risk among these banks has been limited. In particular, the authors extended on the extreme value theory to focus on the wider impact of spillover, including significant direction of contagion from non-Islamic banks to Islamic banks.

The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies.

The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates.

The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods.

The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates.

This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.