Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak

In this study, we examine oil price extreme tail risk spillover to individual Gulf Cooperation Council (GCC) stock markets and quantify this spillover’s shift before and during the COVID-19 pandemic. A dynamic conditional correlation generalized autoregressive heteroscedastic (DCC- GARCH) model is employed to estimate three important measures of tail dependence risk: conditional value at risk (CoVaR), delta CoVaR (ΔCoVaR), and marginal expected shortfall (MES). Using daily data from January 2017 until May 2020, results point to significant systemic oil risk spillover in all GCC stock markets. In particular, the effect of oil price systemic risk on GCC stock market returns was significantly larger during COVID-19 than before the pandemic. Upon splitting COVID-19 into two phases based on severity, we identify Saudi Arabia as the only GCC market to have experienced significantly higher exposure to oil risk in Phase 1. Although all GCC stock markets received greater oil systemic risk spillover in Phase 2 of COVID-19, Saudi Arabia and the United Arab Emirates appeared more vulnerable to oil extreme risk than other countries. Our empirical findings reveal that investors should carefully consider the extreme oil risk effects on GCC stock markets when designing optimal portfolio strategies, minimizing portfolio risk, and adopting dynamic diversification process. Policymakers and regulators should also enact awareness, oversight, and action plans to minimize adverse oil risk effects.     

COVID-19, ESG investing,and the resilience of more sustainable stocks: Evidence from European firms

Following the COVID-19 outbreak, orientation toward sustainability is a critical factor in ensuring firm survival and growth. Using a large sample of 1,204 firms in Europe during the year 2020, this study investigates how more sustainable firms fare during the pandemic compared with other firms in terms of risk–return trade-off and stock market liquidity. We also highlight the drivers of the resilience of more sustainable firms to the pandemic. Particularly, we document that higher levels of cash holdings and liquid assets in the pre-COVID period help these firms to perform and absorb the COVID-19 externalities better than other firms. Our results are robust to a host of econometric models, including GMM estimations and several measures of stock market performance. These findings contribute to the theoretical and empirical debate on the role of the sustainability as a source of corporate resilience to unexpected shocks.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

The outbreak of COVID-19 and stock market liquidity: Evidence from emerging and developed equity markets

The outbreak of the novel corona virus has heightened concerns surrounding the adverse financial effects of the outbreak on stock market liquidity and economic policies. This paper contributes to the emerging strand of studies examining the adverse effects of the virus on varied aspect of global markets. The paper examines the causality and co-movements between COVID-19 and the aggregate stock market liquidity of China, Australia and the G7 countries (Canada, France, Italy, Japan, Germany, the UK and the US), using daily three liquidity proxies (Amihud, Spread and Traded Value) over the period December 2019 to July 2020. Our empirical analysis encompasses wavelet coherence and phase-differences as well as a linear Granger causality test. Linear causality test results suggest that a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity. To quantitatively examine the degree of causality between COVID-19 outbreak and stock market liquidity, we employ the continuous wavelet coherence approach with results revealing the unprecedented impact of COVID-19 on stock market liquidity during the low frequency bands for countries that were hard hit with the COVID-19 outbreak, i.e., Italy, Germany, France, the UK and the US. Further, evidence shows that there is a heterogeneous lead-lag nexus across scales for the entire period of the study.     

Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic

Using the five-minute interval price data of two cryptocurrencies and eight stock market indices, we examine the risk spillover and hedging effectiveness between these two assets. Our approach provides a comparative assessment encompassing the pre-COVID-19 and COVID-19 sample periods. We employ copula models to assess the dependence and risk spillover from Bitcoin and Ethereum to stock market returns during both the pre-COVID-19 and COVID-19 periods. Notably, the COVID-19 pandemic has increased the risk spillover from Bitcoin and Ethereum to stock market returns. The findings vis-à-vis portfolio weights and hedge effectiveness highlight hedging gains; however, optimal investments in Bitcoin and Ethereum have reduced during the COVID-19 pandemic, while the cost of hedging has increased during this period. The findings also confirm that cryptocurrencies cannot provide incremental gains by hedging stock market risk during the COVID-19 pandemic.     

The transition of the global financial markets' connectedness during the COVID-19 pandemic

This study contributes to the literature on financial research under the presence of the COVID-19 pandemic. Fresh evidence emerges from using two novel approaches, namely network analysis and wavelet coherence, to examine the connectedness and comovement of financial markets consisting of stock, commodity, gold, real estate investment trust, US exchange, oil, and Cryptocurrency before and during the COVID-19 onset. Moreover, unlike the previous studies, we seek to fill a gap in the literature regarding the ex-post detection of COVID-19 crises and propose the Markov-switching autoregressive model to detect structural breaks in financial market returns. The first result shows that most financial markets entered the downtrend after January 30, 2020, coinciding with the date the World Health Organization (WHO) declared the COVID-19 pandemic as a Public Health Emergency of International Concern. Thus, it is reasonable to use this date as the break date due to COVID-19. The empirical result from network analysis indicates a similar connectedness, or the network structure, in other words, among global financial markets in both the pre-and during COVID-19 pandemic periods. Moreover, we find evidence of market differences as the MSCI stock market plays a central role while Cryptocurrency presents a weak role in the global financial markets. The findings from the wavelet coherence analysis are quite mixed and illustrate that the comovement of the financial markets varies over time across different frequencies. We also find the main and most significant period of coherence and comovement among financial markets to be between December 2019 and August 2020 at the low-frequency scale (>32 days) (middle and long terms). Among all market pairs, the oil and commodity market pair has the strongest comovement in both pre-and during the COVID-19 pandemic phases at all investment horizons.     

The impact of COVID-19 on the G7 stock markets: A time-frequency analysis

We examine the co-movement of the G7 stock returns with the numbers of confirmed COVID-19 cases and causalities based on daily data from December 31, 2019 to November 13, 2020. We employ the wavelet coherence approach to measure the impact of the numbers of confirmed cases and deaths on the G7 stock markets. Our findings reveal that both the number of confirmed COVID-19 cases and the number of deaths exhibit strong coherence with the G7 equity markets, although we find heterogeneous results for the Canadian and Japanese equity markets, in which the numbers of COVID-19 cases and the deaths exhibit only a weak relationship. This evidence is more pronounced in the long-term horizon rather than the short-term horizon. Moreover, the lead-lag relationship entails a mix of lead-lag relations across different countries. We present the implications of these findings for both policymakers and the international investment community.     

Herding behavior in the cryptocurrency market during COVID-19 pandemic: The role of media coverage

This paper examines herding behavior in the cryptocurrency market during the COVID-19 pandemic using daily data and based on static and regime-switching models. Furthermore, we investigate whether herding behavior is affected by the coronavirus media coverage. Based on a sample of the top-43 cryptocurrencies in terms of market capitalization between 2013 and 2020, we find significant evidence of herding for the entire sample period only during high volatility state. Moreover, during the COVID-19 crisis, results suggest that investors in the cryptocurrency market follow the consensus. Finally, the impact of coronavirus media coverage is significant on herding among investors, explaining such behavior in the cryptocurrency market during the COVID-19 crisis. Our findings explain herding determinants that may help investors avoid such comportment, mainly during the crisis.     

Topological properties of reconstructed credit networks and banking systemic risk

Financial bipartite networks provide channels for contagion risks and their topological properties determine financial stability. We enrich the bipartite network reconstruction methods proposed by Ramadiah et al. (2020) and extend them to the Chinese banking system. By comparing the reproducibility of the real credit market and the corresponding systemic risk, the impact of topological properties for different reconstructed bipartite networks on financial stability is analyzed. The empirical evidence shows that network reconstruction methods based on maximum entropy ensembles capture more properties in the real credit network. It also highlights that the different systemic risk level is mainly contributed by the topological properties based on common exposures. These analyses for topological properties provide regulatory insights for systemic risk prevention. It shows that reducing credit similarity across banks while increasing credit diversification in different sectors helps to control systemic risk. The results imply the possibility of increasing financial stability through the macro-regulation of the credit market structure.     

保障性住房项目财务风险控制研究

保障性住房项目建设过程中需要的资金量比较大,质量要求高,财务风险比较高,尤其是2020年新冠肺炎疫情的爆发,加大了保障性住房项目建设的风险。论文立足于新冠肺炎疫情环境下保障性住房项目财务风险管理的特点,阐述保障性住房项目所存在的财务风险,最后提出控制保障性住房项目财务风险的具体对策。     

On single point forecasts for fat-tailed variables

We discuss common errors and fallacies when using naive “evidence based” empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management.We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).     

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.     

Nexus between drivers of COVID-19 and country risks

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.     

Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.     

Economic fundamentals,policy responses,and state-level municipal bond sensitivity to COVID-19 prevalence

This paper conducts a state-by-state analysis of the financial impact of the COVID-19 pandemic on the U.S. municipal bond market. Using panel regressions and state-by-state regressions, we find that the prevalence rates of the COVID-19 virus negatively impacted the aggregate performance of municipal bonds. The study also explored whether the disparities in the economic fundamentals of U.S. states, as well as the COVID-19 mitigation policies employed by each state, can explain the sensitivity of the state’s municipal bond to its COVID-19 prevalence rates. States with more desirable economic fundamentals and robust COVID-19 mitigation policies appeared to have higher COVID-19 sensitivities than states that do not. This finding may be due to a baseline effect, in which the higher levels of economic activities in these states also make them more susceptible to the deleterious effects of the stronger mitigation policies enacted by them.     

Forecasting systemic impact in financial networks

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. So-called systemic risk betas account for a company’s position within the network of financial interdependencies, in addition to its balance sheet characteristics and its exposure to general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast the systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients.     

COVID-19 related media sentiment and the yield curve of G-7 economies

We explore the connectedness of the components of the sovereign yield curve (slope, level and curvature) across G-7 countries and media sentiment about COVID-19. The recent pandemic is a unique opportunity to identifying the transmitters and receivers of risk. Our results indicate that media sentiment along with the US yield curve components are main transmitter of spillovers, whereas Japan is the leading recipient of spillover. Among the European countries, we notice France as a major transmit, whereas Germany and UK switch role as transmitter and receiver alternatively. The results are important for mapping the interconnectedness between countries. In addition, policy makers can use them when devising disaster plans to prepare for future market crises.     

Impact of the COVID-19 outbreak and its related announcements on the Chinese conventional and Islamic stocks’ connectedness

In this paper, we assess the impacts of the COVID-19 counts (infected cases, deaths and recovered) and related announcements on the Islamic and conventional stocks interplays in the Chinese market. We test whether Islamic stocks are perceived as assets providing diversification benefits in time of COVID-19 pandemic. Doing so, we implement a multivariate GJR-GARCH model under dynamic conditional correlation (DCC) as well as multiple and partial wavelet coherence methods to recent Chinese daily data ranging from 2 December 2019 to 8 May 2020 and COVID-19 related announcement for the period. Our results from multivariate GJR-GARCH models reveal that COVID-19 infected cases and deaths do impact mean DCCs between Islamic and conventional stocks, number of recovered do not have such impact, while none of the above have any significant impact on the DCCs fluctuations. However, when we analyze the impact of COVID-19 related announcement on the variation of conditional correlation between two stocks (i.e. DCC volatility) our findings show that 7 out of 10 such announcements (mainly those with serious health treats or economic implications) do effect those volatilities in Chinese equity market. The empirical findings from partial and multiple wavelet coherences provide robust evidence of instability in the co-movement between Islamic and conventional indexes for different scales and over dissimilar sub-periods. Indeed, the weakening of co-movements is especially notable in the very short and short-run where operating the short-term investors. Our empirical findings offer several key propositions for policy makers and portfolio managers in China with broad implications applicable to other markets.     

Investor protection,regulation and bank risk-taking behavior

This paper examines whether the influence of investor protection on banks’ risk is channeled through banking regulation, and vice-versa, using panel data from a sample of 567 European and US banks for the 2004–2015 period. As banking regulatory factors, we consider capital stringency, activity restrictions and private monitoring, whereas as investor protection factors, we consider the level of shareholder and creditor protection. We find that banking regulation moderates the positive direct influence of investor protection on banks’ risk, while investor protection reinforces the negative direct influence of banking regulation on risk. Moreover, we show that the negative effect of national regulations on banks’ risk is more pronounced during systemic crisis years. Finally, taking into account market competition, we argue that private monitoring only has a direct effect on banks’ risk, whereas the effects of capital stringency and activity restriction are channeled through market competition.