Risk transmission between equity market of China and its trading partners: new evidence from various financial crises

PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

Novel Method for Estimating Time-Varying COVID-19 Transmission Rate

The transmission rate is an important indicator for characterizing a virus and estimating the risk of its outbreak in a certain area, but it is hard to measure. COVID-19, for instance, has greatly affected the world for more than 3 years since early 2020, but scholars have not yet found an effective method to obtain its timely transmission rate due to the fact that the value of COVID-19 transmission rate is not constant but dynamic, always changing over time and places. Therefore, in order to estimate the timely dynamic transmission rate of COVID-19, we performed the following: first, we utilized a rolling time series to construct a time-varying transmission rate model and, based on the model, managed to obtain the dynamic value of COVID-19 transmission rate in mainland China;second, to verify the result, we used the obtained COVID-19 transmission rate as the explanatory variable to conduct empirical research on the impact of the COVID-19 pandemic on China's stock markets. Eventually, the result revealed that the COVID-19 transmission rate had a significant negative impact on China's stock markets, which, to some extent, confirms the validity of the used measurement method in this paper. Notably, the model constructed in this paper, combined with local conditions, can not only be used to estimate the COVID-19 transmission rate in mainland China but also in other affected countries or regions and would be applicable to calculate the transmission rate of other pathogens, not limited to COVID-19, which coincidently fills the gaps in the research. Furthermore, the research based on this model might play a part in regulating anti-pandemic governmental policies and could also help investors and stakeholders to make decisions in a pandemic setting.

Impact of Pandemic Outbreak and Targeted Required Reserve Ratio Cut Policy on Stock Return Rate: Evidence from China

This study analyzed the impact of COVID-19 outbreak and targeted required reserve ratio cut policy on stock returns of Chinese listed companies. This paper uses the data of 3,449 A-share listed companies from February 3, 2020 to December 31, 2020 for research, the empirical results showed that stock prices of private enterprises with stronger debt-paying ability and looser financing constraints, and state-owned enterprises with less supply chain credit risks performed better, in the central and western regions, enterprises with stronger solvency and looser financing constraints have better stock price performance during the early stages of pandemic. After the implementation of the targeted RRR cut policy, the stock prices of enterprises with poor solvency, private enterprises, and enterprises in central and western regions with strong financing constraints, state-owned enterprises, and enterprises in eastern region with high credit risks all showed significant reversals, and the stock prices reflected the effect of the targeted RRR cut policy in the short and medium term. Over time, the pandemic has been controlled, and the resumption of work and production has freed most enterprises from financial difficulties. However, due to sporadic outbreaks, large private enterprises and eastern enterprises with strong risk resistance and loose financing constraints enjoy better stock price performance. This study is helpful for enterprises to understand the value of financial flexibility and solvency and provides a reference for enterprises to make financial decisions: how to balance the benefits and costs of solvency. © Tian Wang, Fang Fang and Linhao Zheng, 2023.

Performance of IPOs During Covid-19 Pandemic and Factors Affecting Investors' Perception

Initial Public Offering (IPO) is a fund-raising tool through which a company gets listed for the first time under SEBI regulation and issues IPOs to raise funds from the public. The shift from a privately-owned to a publicly-owned firm via an IPO is the most significant event in a company's life (Pagano et al., 1998). In an IPO investment, there is limited historical data to analyze and predict the future performance of the company;hence it becomes a risky investment for the investors as they cannot predict how the shares will perform in the future. Most companies that go for an IPO are in the growth or expansion phase so it becomes more difficult to predict their market position and performance in the future, which leads to uncertainty in deriving their future value. Also, most IPOs are of companies going through a transitory growth period, and are therefore subject to additional uncertainty regarding their future value. This study analyzes the performance of the IPOs issued during the Covid-19 pandemic, when the markets across the world faced massive disruptions. The IPOs from various sectors like finance, technology, service, infrastructure, food, pharmaceutical and information technology were considered for the study. The study also analyzes the factors affecting investor perception towards investment in an IPO. The study considered the IPOs issued during the pandemic, and their performance on the listing day was measured by considering issue price, listing price and closing price. It was observed that 90% of the IPOs selected performed well during the listing day and 10% underperformed. It was also found that factors like company brand, company sector, fundamental analysis, company ratings, expert opinion and stock market conditions had a positive impact on the investors' decision to invest in an IPO. The study also revealed that factors like risk factor in primary market, returns on IPO on the listing day and Gray Market Premium have no significant impact on the investors' perception.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

Zero-COVID policy and stock market sectoral performance in China

With the outbreak of COVID-19, the Chinese government implemented the "zero-COVID” policy as a measure to curb the spread of the virus. The different measures of the policy include widespread testing, contact tracing, and strict quarantine and isolation protocols. In view of recent changes in COVID-19 trends and other economic indicators, the Chinese government withdrew significant provisions of the zero-COVID policy in China. The present study investigates the sectoral performance of the Chinese stock market after the withdrawal of the zero-COVID policy. The study considers eighteen sectoral indices of the Shenzhen Stock Exchange of China as a sample and applies the event study methodology to study the impact of the policy withdrawal on the stock prices performance. The results of the study indicate that sectors such as hotel, consumer staples, the financial sector, real estate, media, and culture have reported significant positive movement after the withdrawal of the zero-COVID policy, while other sectors such as consumer discretionary, energy, healthcare, information technology, manufacturing, mining, technology, telecom, transportation, utilities, wholesale, and retail have shown insignificant reactions. These results also indicate that when the COVID-19 outbreak happened in China, different sectors of the economy reacted negatively except the retail and wholesale sectors, while with the withdrawal of the zero-COVID policy by the Chinese government, the reaction of investors is optimistic as different sectors are reporting either positive reactions in the stock price movement or no reaction. © Prashant Sharma, Surender Kumar, 2023.

Altın ve Kripto Paraların BÍST100 Endeksi için Hedge ve Güvenli Liman Özellikleri: Covid-19 Salgını Etkileri

Covid-19 salgınının ortaya çıkmasından sonra dünya çapında ekonomik çalkantılar ve şiddetli piyasa düşüşlerinin ortaya çıktığı görülmüştür. Bu dönemde hisse senedi piyasalarına yatırım yapmış ajanlar için hedge ve/veya güvenli liman araçları arayışları artmıştır. Kripto paralar ve altın özellikle gelişmekte olan ülke piyasalarındaki yatırımcılar için hedge ve/veya güvenli liman olma konusunda iyi potansiyellere sahiptir. Bu çalışmada, Borsa Ístanbul için Bitcoin, Etherium, Ripple, Litecoin ve altın piyasalarının hedge ve/veya güvenli liman piyasalar olup olmadığı, GARCH(1,1) hata terimleri varsayımı altında modellenmiş regresyon sistemi yardımı ile araştırılmıştır. Analizlerde örneklem olarak 4 Eylül 2017 – 30 Mart 2022 tarihleri arasındaki günlük frekanslı verilerden faydalanılmıştır. Ayrıca, Covid-19 salgın dönemi etkilerini ayrıştırmak için örneklem iki alt gruba ayrılmış ve tahminler Covid-19 öncesi dönem (31 Aralık 2019 öncesi) ve Covid-19 dönemi (31 Aralık 2019 ve sonrası) için ayrı ayrı analiz edilmiştir.A.B.D. doları cinsinden elde edilmiş kripto para (BTC, ETH, XRP, LTC), altın ve BÍST100 endeks getirileri kullanılarak bulunan tahmin sonuçlarına göre, tüm örneklem için Litecoin zayıf güvenli liman olarak ortaya çıkarken, Covid-19 öncesi dönemde Bitcoin ve Etherium zayıf hedge, Covid-19 salgın döneminde de Etherium zayıf güvenli liman olma özellikleri göstermektedir. Tüm örneklem ve salgın öncesi dönem verileri söz konusu olduğunda, BÍST100 endeks getiri dağılımının %10 çeyrek değerinden az olduğu durumlarda Bitcoin, Etherium ve Ripple güvenli liman piyasalar olarak gözlemlenirken, salgın döneminde altın, BÍST100 endeks getiri dağılımının %1 çeyrek değerinden az olduğu durumlarda güvenli bir liman olarak ortaya çıkmıştır. Fakat tüm analizlere dayanarak, altının genel görünümüyle BÍST100 endeksi için hedge veya güvenli limandan çok bir çeşitlendirici varlık olarak öne çıktığı söylenebilir.Alternate :After the Covid-19 outbreak, economic turmoil and severe market crashes have been observed around the world. During this crisis period, cyriptocurrencies and gold have become potentially good hedge and/or safe haven assets for especially the stock investors in emerging markets. This study investigates whether or not Bitcoin, Etherium, Ripple, Litecoin and gold markets have hedge and/or safe-haven properties for Borsa Ístanbul through a regression system modeled under the assumption of GARCH(1,1) error terms. Daily frequency data covering the period September 4, 2017 through March 30, 2022 is used in the sample analysis. In addition, to separate out the effects of the Covid-19 pandemic on the analysis, full sample is divided into two subgroups and the estimations are made separately for the pre-Covid-19 period (before 31 December 2019) and the Covid-19 period (31 December 2019 and later).According to the estimation results, Litecoin emerges as a weak safe haven for Borsa Ístanbul over the entire sample period, while Bitcoin and Etherium appear to be weak hedges in the pre-pandemic period. During the Covid-19 pandemic period, Etherium is shown to be a weak safe haven for the BÍST100 index. Full sample and pre-pandemic data analysis reveal that, Bitcoin, Etherium and Ripple act as safe-haven markets in some cases when the BÍST100 index returns hit lower than their 10% quantile value. After the outburst of the Covid-19 however, gold seems to act as a safe haven asset for Borsa Ístanbul when the BÍST100 index returns hit lower than their 1% quantile value. Based on the overall estimation results, gold stands out as a diversifier rather than a hedge and/or a safe haven asset for the BÍST100 index.

Volatility spillovers among major tourism stock indices during Covid-19 pandemic

This paper examines the dynamics of volatility spillovers among five major tourism stock indices during the Covid-19 period. Our paper enriches the current literature as it is the first paper to investigate the volatility spillovers among major global tourism stock indices by adopting Diebold and Yilmaz (2012. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. ), and Barunik and Krehlik (2018. Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16(2), 271–296.) time and frequency domain methods. Results suggest that total spillovers of the tourism stock indices rose significantly during the pandemic. Turkey and Italy are net volatility spillover transmitters, and others are net volatility spillover receivers. Findings of this study also indicates that the effect of volatility transmission among tourism stock markets is temporary (short-lasting). The results suggest that short-term investors and portfolio managers should avoid investing in the tourism indices in the short term.

Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

Is investing in the renewable energy stock market both financially and ESG efficient? A COVID-19 pandemic analysis

The aim of this paper is to provide a tool for finding investments in the stocks of energy firms that achieve both good financial and reasonable environmental, social, and governance (ESG) performance. Our methodology entails two steps and is based on diversification-consistent DEA models. The first step involves constructing a financially efficient frontier of investment portfolios by applying the model originally proposed by Branda (Omega 52:65–76. 10.1016/j.ejor.2007.04.014, 2015). In the second step, a new DEA model is proposed in order to find the ESG-efficient portfolios among the ones already identified in the first step and to rank them with respect to their ESG performance. This model is parameterised by a weighting system that allows us to assign different importance to the various ESG outputs. Additionally, the proposal allows an evaluation of both ESG and financial efficiency related to the financial energy market over two periods (the pre-COVID-19 and COVID-19 periods), considering renewable energy and non-renewable energy firms both jointly and separately. The results support the better financial performance of the renewable energy stock market compared with that of the non-renewable energy market. © 2023, The Author(s).

Weathering the crash: Do customer-company relationships pay off during economic crises?

Do stronger relationships with customers (customer-company relationships [CCR]) help firms better weather economic crises? To answer this question, we examine firm performance during the stock market crashes associated with the two most severe economic crises of the last 15 years-the protracted Great Recession crisis (2008-2009) and the shorter but extreme COVID-19 pandemic crisis (2020). Juxtaposing the predominant expected utility theory perspective with observed deviations in investor behavior during crises, we find that both pre-crash firm-level customer satisfaction and customer loyalty are positively associated with abnormal stock returns and lower idiosyncratic risk during a market crash, while pre-crash firm-level customer complaint rate negatively affects abnormal stock returns and increases idiosyncratic risk. On average, we find that one standard deviation higher CCR is associated with between $0.9 billion and $2.4 billion in market capitalization on an annualized basis. Importantly, we find that these effects are weaker for firms with higher market share during the COVID-19 crash, but not during the Great Recession crash. These results are found to be robust to a variety of alternate model specifications, time periods, sub-samples, accounting for firm strategies during the crises, and endogeneity corrections. When compared to relevant non-crash periods, we also find that such effects are equally strong during the Great Recession crash and even stronger during the COVID-19 pandemic crash. Contributing to both the marketing-finance interface literature and the nascent literature on marketing during economic crises, implications from these findings are provided for researchers, marketing theory, and managers. Supplementary information: The online version contains supplementary material available at 10.1007/s11747-023-00947-1.

Housing market forecasts via stock market indicators.

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.

Exploration of industrial risk contagion characteristics and mechanism under geopolitical events: evidence from China

Based on the TVP-VAR-DY and TVP-VAR-BK models, this article examines the characteristics and mechanisms of systemic risk contagion in the Chinese industries under geopolitical events by selecting data spans from 1 January 2010 to 31 August 2022. First, dynamic analysis of full-sample risk contagion shows that there is a significant climb in total risk during geopolitical events. Then the static analysis of risk contagion in the full sample specifically shows the correlation between risk contagion and industry chain between the financial and real sectors. Besides, the sub-sample analysis illustrates that during geopolitical events such as the Sino-US Trade War, the COVID-19 Pandemic and the Russia-Ukraine Conflict, Chinese industrial stock indexes show short-term risk spillovers from key industries related to geopolitical events, and gradually spread along the industrial chain in the long run compared to the Chinese ‘Stock Market Crash'. Through further mechanistic tests, we find that the irrational behaviour of investors in the market exacerbates short-term risk contagion, while the financial distress of real firms due to financing constraints exacerbates long-term risk contagion. In addition, geopolitical risk, economic uncertainty, and policy uncertainty as macro variables also have an impact on the short-run and long-run risk contagion. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

A shot in the arm: The effect of COVID-19 vaccine news on financial and commodity markets

We analyze the impact of COVID-19 vaccine announcements by leading vaccine companies on the financial and commodity markets from January to December 2020. We show that the vaccine announcements had varied and economically significant impacts on asset prices. The announcements moved interest rates, stock markets in the U.S. and numerous other countries as well as commodities used in transportation and some agricultural commodities. We show that the stock and commodity markets that experienced larger declines at the beginning of the pandemic receive a larger boost from good vaccine news. We also find that the vaccine news affects stock returns through changes in the expectations of the corporate cash flows and the expected equity risk premium. © 2023 The Eastern Finance Association.

Gambling-Motivated Market Attention and Stock Market Volatility

Retail investors show gambling preferences and pay greater attention to the market than individual stocks. Previous studies report a positive and significant relationship between market attention and volatility. This relationship results from the joint effects of attention to investment-motivated and gambling-motivated components. However, the separate roles of these two components have not yet been examined. Hence, this study applied principal component analysis to identify the gambling-motivated component from market attention and gambling-related variables. The investment-motivated component is the regression residual of the market's attention paid to the gambling-motivated component. This study linearly relates these two components to volatility. The generalized method of moments regression was used to resolve endogeneity problems and biased estimates. The Google search volume index is a proxy for unobserved retail investors' market attention. Using a daily sample of the Thai market from August 6, 2008, to September 30, 2022 (a total of 3,450 observations), this study found a positive relationship between market attention and stock market volatility. This relationship results from the positive effects of both investment-motivated and gambling-motivated components. Attention to gambling is more influential than attention to investment. The explanatory powers of gambling-attention and investment-attention for volatility were 81.33% and 18.67%, respectively. These effects were less pronounced during the COVID-19 pandemic.

The role of COVID-19 in herding: evidence from the Croatian stock market

This paper explores the role of the COVID-19 pandemic on the herding behaviour across market participants in the Croatian market. The analysis uses daily prices of the Croatian stock index, spanning the period January 2016 to December 2022. The hypothesis of the herding behaviour is tested through the quantile regression approach. The findings document no evidence of herding prior to the pandemic crisis. In contrast, herding is discovered during the COVID-19 period. The paper provides policymakers and investors with valuable information to draw significant measures in their investment portfolio management during crises and pandemics. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Research on price transmission in Chinese mining stock market: Based on industry

The strong impact of COVID-19 on the global mining market has caused severe fluctuations in the prices of mineral products and mining stocks. Meanwhile, geopolitical conflicts have exacerbated risks in minerals trade and mining stock transactions. In the face of uncertainties in the international economic landscape and volatility of stock prices, China, as the world's major mineral trading country, has become increasingly linked between its stock market and the mining economy. To clarify the characteristics of mining stock price fluctuations and the evolution of the transmission relationships, and identify the key nodes and main paths of price transmission, we select 100 Chinese mining stocks from January 2019 to October 2022, distinguish them according to the industry category, and use Granger causality test, minimum spanning tree model and complex network analysis method to study. The results show that: (1) Chinese mining stock prices have risen significantly since 2020, and there has been a "decoupling” phenomenon within the stock market, that is, the linkage between some mining stocks has weakened. (2) The stock price fluctuation characteristics and transmission effects of different mining industries are obviously different. Precious metal minerals (PM) have the most dramatic changes in price fluctuations, the most prominent hedging characteristics, and the rapid price response ability, which is the first to accept price transmission. rare earth and rare metal minerals (RE) are sensitive to price fluctuations and are usually the "leader” of the transmission path. Bulk non-ferrous minerals (BNFM) have the most stable price fluctuations and are closely related to other stocks, which is a "transit warehouse” in the transmission path. (3) The price transmission mechanism of Chinese mining stock market has gradually stabilized, and the main transmission paths of "Coal→Agricultural minerals (Agri)→BNFM→Steel” and "PM, Core minerals for new energy (NEM), and RE→BNFM” have been formed in 2022.

Race for Immunity: the Russian Stock Market during the COVID-19 Pandemic

This paper evaluates the stock market performance in Russia during the COVID-19 pandemic from January 3, 2020, to April 30, 2021. The study shows daily abnormal returns of stocks declined on the day when the first coronavirus case was detected. The government's actions in implementing lockdown restrictions and providing stimulus packages positively influenced the stock market. However, the early lifting of lockdowns sent a negative signal to investors. Despite the skepticism among the population toward the first coronavirus vaccine Sputnik V, the news of its registration, mass vaccination, and Russia's "vaccine diplomacy" contributed to the stock market's growth.