Journal articles on the topic 'Volatilité (finances) – Chine – 1990-2020'

Commodity exports depend on global demand and prices, but the increasing volatility of real exchange rates (RER) introduces an additional factor. Thus, this paper studies the RER volatility dynamics, estimated through GARCH and IGARCH models for Brazil, Chile, New Zealand, and Uruguay from 1990 to 2013. We study the impact of RER volatility on total exports using Johansen?s methodology, including proxies for global demand and international prices. The results suggest that exports depend positively on global demand and international prices for all countries; however, conditional RER volatility resulted significant and negative only for Uruguay, in the short- and long-run.

Thailand is a significant global exporter of cassava, of which cassava chips are the main export products. Moreover, China was the most important export market for Thailand from 2000 to 2020. However, during that period, Thailand confronted fluctuations in the cassava product price, and cassava chips were a product with significant price volatility, adapting to changes in export volumes. This study aims to analyze the volatility of the price of cassava chips in Thailand from 2010 to 2020. The data were collected monthly from 2010 to 2020, including the price of cassava chips in Thailand (Y), the volume of cassava China imported from Thailand (X1), the price of the cassava chips that China imported from Thailand (X2), the price of the cassava starch that China imported from Thailand (X3), the substitute crop price for maize (X4), the substitute crop price for wheat (X5), and Thailand’s cassava product export volume (X6). The volatility and the factors affecting the volatility in the price of cassava chips were calculated using Bayesian GARCH-X. The results indicate that the increase in X1, X2, X3, X4, and X6 led to an increase in the rate of change in cassava chip price volatility. On the other hand, if the substitute crop price for wheat (X5) increases, then the rate of change in the volatility of the cassava chip price decreases. Therefore, the government’s formulation of an appropriate cassava policy should take volatility and the factors affecting price volatility into account. Additionally, the government’s formulation of agricultural policy needs to consider Thailand’s macro-environmental factors and its key trading partners, especially when these environmental factors signal changes in the price volatility of cassava.

The study of forecasting volatility of stocks has been discussed and investigated among scholars. Volatility plays important role in determining stock value as well as portfolio in stock market. This study investigates the use of GARCH model (generalized autoregressive conditional heteroskedasticity) in forecasting Islamic index stock in Asian countries. This study employs data from yahoo. finance including six countries namely India, Singapore, Japan, China, Malaysia, and Indonesia. There are 1304 data observation of daily closing price for the period between January 2016 and December 2020. The results of the study show that GARCH model can be employed as a mediation of forecasting sharia indexed stock. This implies that GARCH model can be used as forecasting steps in Islamic stock in Asian countries. Investors can take into account the model of GARCH in forecasting of Islamic stock market in Asian countries particularly India, Japan, China, Singapore, Malaysia and Indonesia

ABSTRAK Penelitian ini bertujuan untuk menguji spillover effect indeks saham syariah Amerika Serikat dan Cina terhadap indeks saham syariah ASEAN dengan menggunakan metode Nonlinier Autoregressive Distributed Lag (NARDL) untuk menemukan spillover effect yang bersifat asimetris selama pandemi Covid-19. Data yang diamati dimulai pada 1 Januari 2020 sampai dengan 30 September 2021 dengan total observasi sebanyak 336 data untuk masing-masing indeks saham. Penelitian ini mendapatkan beberapa temuan. Pertama, indeks saham syariah Amerika Serikat dan Cina memiliki pengaruh asimetris jangka pendek terhadap indeks saham syariah Indonesia, Malaysia dan Thailand selama pandemi Covid-19. Kedua, indeks saham syariah Amerika Serikat dan Cina hanya memiliki pengaruh asimetris jangka panjang terhadap indeks saham syariah Malaysia selama pandemi Covid-19. Ketiga, efek ketika terjadi penurunan indeks saham syariah Amerika Serikat dan Cina lebih besar dibandingkan pada saat terjadi kenaikan terhadap indeks saham syariah Malaysia selama pandemi Covid-19. Salah satu penyebab hubungan tersebut adalah karena adanya hubungan dagang yang sangat erat antara Amerika Serikat dan Cina terhadap Malaysia. Adapun implikasi dari penelitian ini adalah investor internasional dapat menjadikan hasil penelitian ini sebagai bahan pengambilan keputusan apabila terjadi kontraksi akibat krisis seperti pada saat pandemi Covid-19 terhadap indeks saham syariah Amerika Serikat dan Cina untuk mempertahankan maupun menjual portofolio investasi mereka. Kata Kunci: Spillover, Indeks Syariah, Asimetris, Covid-19. ABSTRACT This study aims to examine the spillover effect of Islamic stock indexes of the United States and China on the ASEAN Islamic stock index using the Nonlinear Autoregressive Distributed Lag (NARDL) method to find asymmetric spillover effects during the Covid-19 pandemic. The observed data starts on January 1, 2020, until September 30, 2021, with a total of 336 observations for each stock index. This study found some findings. First, the Islamic stock indexes of the United States and China have a short-term asymmetric influence on the Islamic stock indices of Indonesia, Malaysia, and Thailand during the Covid-19 pandemic. Second, the Islamic stock indexes of the United States and China have only a long-term asymmetric influence on Malaysia's sharia stock indexes during the Covid-19 pandemic. Third, the effect when there is a decline in Islamic stock indexes of the United States and China is greater than when there is an increase in the Malaysian sharia stock index during the Covid-19 pandemic. One of the reasons for this relationship is the very close trade relationship between the United States and China with Malaysia. The research implication of this study is that international investors can use the results of this research as a decision-making material in the event of a contraction due to the crisis (one of which is the Covid-19 pandemic) in the United States and China Islamic stock indexes to maintain or sell their investment portfolios. Keywords: Spillover, Islamic Index, Asymmetric, Covid-19. DAFTAR PUSTAKA Abdullahi, S. I. (2021). Islamic equities and covid-19 pandemic: Measuring Islamic stock indices correlation and volatility in period of crisis. Islamic Economic Studies, 29(1), 50-66. https://doi.org/10.1108/IES-09-2020-0037 Aslam, F., Mohmand, Y. 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This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

Investor sentiment is the result of trading behavior and irrational beliefs of investors leading to high volatility and market mispricing. This review aims to study the entire spectrum of articles in the domain of investor sentiment using a bibliometric analysis approach. To this end, the study analyzes a total of 1,919 articles published in the Scopus database between 1979 and 2022. The review uncovers major themes, leading authors, influencing articles, trend topics, top contributing countries, and affiliations. The review shows that the research in the domain of investor sentiment is growing exponentially with an annual growth rate of 15.88%, and the year 2020 witnessed the highest number of scientific productions accounting for 252 (13.68%) total publications. The results display that the USA and China are leading countries in terms of the total contribution and volume of studies from respective authors. The review also reveals that existing research in the field has mainly focused on themes such as market efficiency, asset pricing, stock returns, sentiment analysis, IPO underpricing, overreaction, and volatility, whereas Covid-19 and Bitcoin depicted as emerging themes from recent scholarly works.

The advent of the VUCA era and the development of digital finance (DF) present opportunities and challenges for manufacturing companies to seek sustainable innovation by increasing their organizational resilience (OR) to withstand crises. The production, flow, and acquisition of corporate knowledge are indispensable to the establishment of organizational resilience. In this paper, we analyze how to make manufacturing enterprises more courageous and innovative in the context of digital finance. We used a perspective of knowledge channel acquisitions to achieve this aim. Using a sample of 1965 manufacturing companies in China from 2013 to 2020, we analyzed whether greater enterprise knowledge (internal knowledge and external knowledge) can yield higher levels of innovation performance and whether organizational resilience plays a role in the context of digital finance. The results show that (1) both internal enterprise knowledge (IEK) and external enterprise knowledge (EEK) have a significant positive impact on the sustainable innovation performance of manufacturing enterprises; (2) organizational resilience has a mediation role in the process of promoting sustainable innovation performance through enterprise knowledge; (3) digital finance significantly enhances the impact of enterprise knowledge on long-term growth and financial volatility of organizational resilience, and significantly positively moderates the mediation effect of organizational resilience; and (4) digital finance support policies issued by the government significantly improve the sustainable innovation performance of manufacturing firms. Based on these results, manufacturing firms can improve innovation performance by enhancing organizational resilience. This paper contributes to this field of research by providing an analysis of manufacturing firms, presenting a new view on the improvement of innovation performance in the context of digital finance.

Since the first case was reported at the end of 2019, COVID-19 has spread throughout the world resulting in more than 2 million confirmed cases. The World Health Organization (WHO) also declared the COVID-19 disease as pandemic on 11 March 2020. The COVID-19 pandemic has also affected the global financial market, which includes Malaysia. This study aims to investigate the impact of the COVID-19 outbreak on the Malaysian stock market. The dependent variables used in this study were the Kuala Lumpur Composite Index (KLCI) and 13 other sectorial indices. The independent variables were (i) the number of COVID-19 cases in Malaysia, China, and USA; (ii) the number of deaths due to COVID-19 in Malaysia, China, and USA; (iii) the volatility index, and (iv) the Brent oil price. The sample period of this study covered from 31st December 2019 to 18th April 2020. The findings showed that higher numbers of COVID-19 cases in Malaysia tended to adversely affect the performance of the KLCI index and all sectorial indices, except for the Real Estate Investment Fund (REIT) index. The results also showed that the Brent oil price and the volatility index tended to affect the Malaysian stock market performance. The results of this study can help investors understand the impact of COVID-19 on different sectors in Malaysia.

With the continuous increase of China’s foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China’s stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China’s stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China’s stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China’s industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages.

Purpose Recent empirical studies by Antonakakis, Chatziantoniou and Filis (2013), Brogaard and Detzel (2015) and Christou et al. (2017) present evidence, which supports the notion that a rise in economic policy uncertainty (EPU) will lead to a decline in stock prices. The purpose of this paper is to examine US categorical policy uncertainty on stock returns while controlling for implied volatility and downside risk. In addition to the domestic impacts of policy uncertainty, this paper also presents evidence that changes in US policy uncertainty promptly propagates to the global stock markets. Design/methodology/approach This study uses a GED-GARCH (1, 1) model to estimate changes of uncertainties in US monetary, fiscal and trade policies on stock returns for the sample period of January 1990–December 2018. Robustness test is conducted by using different set of data and modeling techniques. Findings This paper contributes to the literature in several aspects. First, testing of US aggregate data while controlling for downside risk and implied volatility, consistently, shows that responses of stock prices to US policy uncertainty changes, not only display a negative effect in the current period but also have at least a one-month time-lag. The evidence supports the uncertainty premium hypothesis. Second, extending the test to global data reveals that US policy uncertainty changes have a negative impact on markets in Europe, China and Japan. Third, testing the data in sectoral stock markets mainly displays statistically significant results with a negative sign. Fourth, the evidence consistently shows that changes in policy uncertainty present an inverse relation to the stock returns, regardless of whether uncertainty is moving upward or downward. Research limitations/implications The current research is limited to the markets in the USA, eurozone, China and Japan. This study can be extended to additional countries, such as emerging markets. Practical implications This paper provides a model that uses categorical policy uncertainty approach to explain stock price changes. The parametric estimates provide insightful information in advising investors for making portfolio decision. Social implications The estimated coefficients of changes in monetary policy uncertainty, fiscal policy uncertainty and trade policy uncertainty are informative in assisting policymakers to formulate effective financial policies. Originality/value This study extends the existing risk premium model in several directions. First, it separates the financial risk factors from the EPU innovations; second, instead of using EPU, this study investigates the effects from monetary policy, fiscal policy and trade policy uncertainties; third, in additional to an examination of the effects of US categorical policy uncertainties on its own markets, this study also investigates the spillover effects to global major markets; fourth, besides the aggregate stock markets, this study estimates the effects of US policy uncertainty innovations on the sectoral stock returns.

China has formed a new C2F (company-to-farmer) model of internet and agriculture. How to build a sustainable linkage of the C2F platform is important for promoting agricultural industrialization. Based on the cognition theory and internet thinking, we characterized the linkage mechanism and stability framework of the C2F regarding default proportion, benefits fairness and benefits gap. Using the logistic regression method, we constructed the impact effect model of benefit links stability based on the farmers’ characteristics, platform cognition and social environment. We found that in the C2F, optimizing farmers’ age structure (17.93%, impact effect), increasing farmers’ income level (16.79%), as well as improving farmers’ education level (14.33%), policy support (11.35%), platform service quantity (9.82%), market volatility (9.11%), platform transaction transparency (9.07%), farmers’ risk tolerance (7.93%), and platform technical guidance effect (3.67%) had a significant impact on reducing default proportion (28.13%) and benefits gap (36.55%), thus heightening benefits fairness (35.32%). The research suggested, we should promote the sustainability of C2F by improving the farmers’ digital ability and platform function, developing innovative linkage mechanisms between companies and farmers, strengthening government guidance, and protecting the policy environment.

PurposeUsing intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).Design/methodology/approachThe authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.FindingsThe empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.Originality/valueThe study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

PurposeThe authors aim to examine the mean and volatility linkages between the gold market and the Latin American equity markets in the entire sample period and two crises periods, namely the US financial crisis and the Chinese crash.Design/methodology/approachTo examine the return and volatility spillovers, the authors employ VAR-BEKK-GARCH model on the daily data of four emerging Latin American equity markets which include Peru, Chile, Brazil and Mexico, which ranges from January 2000 to June 2018.FindingsThe results show that the return transmissions vary across the stock markets and the crises periods. The volatility transmission is found to be bidirectional between the gold and stock markets of Brazil and Chile during the US financial crisis. Furthermore, the volatility spillover is unidirectional from Brazil to gold and from gold to Peru stock market during the Chinese crash. We also calculate the optimal weights hedge ratios for gold and stock portfolio. The result suggests that portfolio managers need to increase the weight of gold for the equity portfolios of Peru and Mexico during the US financial crisis. Furthermore, during the Chinese crisis, investors may raise the investment in gold for the equity portfolios of Brazil and Chile. Finally, the cheapest hedging strategy is CHIL/GOLD during the US financial crisis, whereas MEXI/GOLD during the Chinese crash.Practical implicationsThese findings have useful insights for portfolio diversification, asset pricing and risk management.Originality/valueThe study's outcome provides policymakers and investors with in-depth insights regarding hedging, risk management and portfolio management.

PurposeThe 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.Design/methodology/approachIn 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.FindingsThe 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.Research limitations/implicationsThe study may be extended to include other emerging market economies under a dynamic framework.Practical implicationsResearchers 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.Originality/valueCOVID-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.

In this study, it was analysed to determine whether the coronavirus, which became a global epidemic by affecting the whole world in a short time, caused any changes in the volatility and liquidity of stock market indices in the USA, Germany, China, Japan, Egypt, and Turkey. In this context, the effects of the coronavirus epidemic on DOW30 Index in USA, DAX Index in Germany, SSE Composite Shanghai Index in China, NIKKEI 225 Index in Japan, EGX30 Index in Egypt and BIST100 Index in Turkey were investigated. The results and estimations of the study were limited to the relevant countries, and this was the limitation of the study. Selected countries for the analysis were determined by their locational and financial market properties among developed and developing countries which were the most representative ones. The date of the first case for each country announced by WHO was taken as a basis date. A data set was prepared for the period from the first case had been seen to 18 November 2020 for each country. In order to determine the pre-pandemic and post-pandemic differentiation, a pre-pandemic period data set was created as well as the same amount of data. Thus, it was tried to determine whether there was a differentiation for the period before and after the pandemic. The return and liquidity series of the indices were estimated with GARCH(1,1), one of the conditional variance models, and it was observed that there were changes in the volatility and liquidity of the relevant stock market indices after COVID-19. In addition, volatility clusters were observed. Return series of all country stock market indices which were the subject of the research had determined to have thick tail and skewness features like classical financial time series.

PurposeAccording to the Government Response tracker (oxCGRT) index, the overall government response, stringency, economic support, containment and health policies to COVID-19 from January 2020 to December 2022. The main objective of this paper is to explore how stock market performance is affected by these polices, respectively.Design/methodology/approachThe authors employ EGARCH and autoregressive distributional lag (ARDL) models to test the impact of epidemic prevention policy implementation on stock market returns, volatility and liquidity and make cross-country comparisons for six important world economies.FindingsFirstly, the implementation of various preventive policies hurts stock market returns and increases volatility, but there are a few indicators that have no effect or have an easing effect in some countries. Secondly, health policies exacerbate market volatility and have a stronger effect than other policy indicators. Thirdly, In China and the USA, anti-epidemic policies have been shown to worsen liquidity, while in Japan they have been shown to improve liquidity.Originality/valueFirst, enrich the growing body of COVID-19 research by comprehensively examining whether and how government prevention policies affect stock market returns, volatility and liquidity. Second, explore the impact of different types of intervention policies on stock market performance, separately.

This paper investigates the determinants of crude oil in the world’s top five economies, including the USA, China, Germany, Japan and India. To examine the impact of the exchange rate, oil imports, oil demand by the commercial sector, oil demand by the transport sector, oil demand by the power sector, industrial production and oil inventory on crude oil prices of the top five economies have been studied from 1990 to 2020. We applied the Autoregressive Distributed Lag (ARDL) model for the analysis of the research. Panel results showed a significant impact of the exchange rate in the long and short run on crude oil prices. On the other hand, in case of Germany’s Industrial production, exchange rate, oil imports, and demand for oil by the power sectors have a significant effect on the prices of crude oil. In the case of India, industrial production, exchange rate, oil consumption, investment in oil, import of oil and demand for oil by the transport sectors have a significant effect on the prices of crude oil. In the case of the USA, crude oil inventories have a significant impact on crude oil prices in the long run. In the case of Japan’s exchange rate, oil consumption has a substantial effect on crude oil prices in both the long run and short run. We found that the exchange rate is the most significant factor which influences crude oil prices in all five countries.

Purpose The purpose of this paper is to investigate the effects of exchange rate volatility, oil price return and COVID-19 cases on the stock market returns and volatility for selected emerging market economies. Additionally, this study compares the market performance in the emerging economies during the COVID-19 pandemic with the pre-COVID and global financial crisis (GFC) period. Design/methodology/approach The authors apply the arbitrage pricing theory to model the risk-return relationship between the risk-based factors (exchange rate volatility and COVID-19 cases) and stock market returns. By applying the exponential generalized autoregressive conditional heteroskedasticity model, the study captures the asymmetric volatility spillover from the stock markets to foreign exchange markets and vice versa. Findings Findings reveal that exchange rate volatility exerts a negative and significant effect on the market returns in Brazil (BOVESPA), Chile (S&P CLX IPSA), India (SENSEX), Mexico (S&P BMV IPC) and Russia (MOEX) during the coronavirus pandemic. Regarding the effect of oil price returns, the authors find a positive relationship between oil price and stock market returns across all the economies in the study. The market returns of Russia, India, Brazil and Peru appeared more volatile during the pandemic than the GFC period. Practical implications As the exchange rate volatility is causing higher risk and uncertainty in the stock market’s performance, the central bank’s effort to maintain a stabilizing effect on the exchange rate sale can be proven crucial for the economies under consideration. Emphasized should also be given to boost investors’ confidence in the stock market, and for this, the government policy actions in reducing the transmission of the disease are the need of the hour. Originality/value While a large volume of literature on stock market performance in times of COVID-19 has emerged from developed economies, this study adds to the literature by exploring the emerging economies’ stock market performance during the COVID-19 pandemic. Unlike previous literature, this study examines the volatility spillover between stock and exchange rate markets in the worst affected emerging economies during the crisis.

PurposeThe study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.Design/methodology/approachThe study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).FindingsThe results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.Research limitations/implicationsIn Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.Originality/valueThe results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

Purpose This paper aims to explore the impact of the coronavirus on the volatility spillovers of 10 selected developed markets hit by this pandemic (e.g. the USA, Canada, Korea, Japan, the UK, Germany, Italy, Spain, France and China). Design/methodology/approach The database consists of daily data from January 1, 2020, to December 31, 2022. The data used are the precise daily closing prices of various indices of selected markets gathered from the DataStream and Investing.com databases. The authors use the VAR model to study the transmission of volatility between stock markets and analyze the dynamic links between them. Then, the Granger causality test is used to study the volatility movements and determine which of these markets is likely to influence the others. Then, impulse response functions are used to understand the reactions of the studied markets following shocks in the two most important markets, namely, the American and Chinese markets. Finally, forecast errors variance decomposition is used to measure the dynamic interactions that characterize the relationships between the studied markets. Findings Empirical results reveal instability in the returns of various indexes and the existence of causal relationships between standardized volatility of markets. The reactions of some markets following a shock in American and Chinese markets differ among markets. The empirical results also show that forecast errors variance of some markets begin coming from their own innovations during first periods. These shares decrease then in favor of other markets interventions. Practical implications The findings have significant practical implications for governments around the world as well as for financial investors. The successful practice of China’s pandemic prevention and control efforts may inspire governments to determine how to overcome panic and strengthen confidence in victory. Policymakers can use the insights from our study to design more effective economic policies and regulations to mitigate the negative impact of future pandemics on the financial system. Regulators can use these results to identify areas of weakness in the financial system and take proactive measures to address them. Financial investors may use the outcomes of our result to better understand the impact of global pandemics on financial markets. They may know which markets are the most active, which ones are causing considerable effects on the others and which ones show resilience and an anti-risk capacity. This may help them to make appropriate decisions about their investments. Originality/value It has become imperative to estimate the impact of this pandemic on the behavior of financial markets to prevent the deterioration and dysfunction of the global financial system. The findings have important implications for financial investors and governments who should know which markets are the most shaken, which cause remarkable effects on others and which show resilience and anti-risk capacity. Countries could follow China in some measures taken to moderate the negative effects of this epidemic on national economies.

PurposeAlthough some research has been carried out on feedback trading in different asset classes, there have been few empirical investigations that consider both major and emerging stock markets (Koutmos, 1997; Antoniou et al., 2005; Kim, 2009) stock index futures (Salm and Schuppli, 2010). In this study, the author examines positive/negative feedback trading in both developed-emerging-frontier-standalone (51) stock markets for 2010–2020 and sub-periods including COVID-19 period.Design/methodology/approachThe hypothesis “feedback trading behaviour led the price boom/bust in the stock markets during the first quarter of COVID-19 pandemic” is tested by employing the Sentana and Wadhwani (1992) framework and using asymmetrical GARCH models (GJRGARCH, EGARCH) in accordance with the empirical literature.FindingsThe following conclusions can be drawn from the present study; (1) There is no evidence to support a significant distinction between developed, emerging, frontier or standalone markets or high/upper middle, lower middle income economies in the case of feedback trading. It is more likely to be a general phenomenon reflecting the outcomes of general human psychology (2) in the long term (2010–2020) based on the feedback trading results Asian stock markets appear to be far from efficiency.Research limitations/implicationsStock markets are selected based on data availability.Practical implicationsSeveral inferences can be drawn about overall results. First, investors and portfolio managers should beware of their investment decisions during bearish market conditions where volatility is on the rise and also when there is a strong reaction to bad news/negative shocks in the market. Moreover, investing in Asia stock markets may require more attention since those markets are reputed to be more “idiosyncratic”, less reliant on economic and corporate fundamentals in their pricing. Moreover, the impact of foreign investors on stock market volatility and returns and weaker implementation of regulations also affect the efficiency of the markets (Lipinsky and Ong, 2014).Originality/valueTo the best of the author’s knowledge, most studies in the field of feedback trading in stock markets have only focused on a small sample of countries and second, the effect of COVID-19 uncertainty on the stock markets have not been addressed in the literature with respect to feedback trading. This paper fills these literature gaps. This study is expected to provide useful insights for understanding the instabilities in stock markets particularly under conditions of high uncertainty and to fill the gap in the literature by comparing the results for a large sample of countries both in the long term and in the pandemic.Highlights for reviewThis study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

Abstract In May 2020, China formulated the Internal Circulation Strategy (ICS) to address the risks of global economic downturns caused by deglobalization. This study is the first to empirically examine the impacts of China's ICS on its stock market performance, focusing on investor behavior. Using data from the Baidu search index and the Shanghai and Shenzhen stock exchanges for the period 27 July 2020 to 5 May 2023, the results reveal that stock returns are negatively associated with investor attention to China's ICS, while stock volatilities are positively associated. This suggests that the ongoing and profound shift in economic strategy has raised concerns among investors. Furthermore, our analysis of heterogeneity finds that the negative association with stock market returns is statistically significant only during times of high policy uncertainty or geopolitical risk. The positive association with stock market volatility is statistically significant only during times of high geopolitical risk. This indicates that the stability of the internal and external environment plays a crucial role in alleviating investor concerns. We also observe mixed impacts on different sectors of the stock markets, with some sectors unaffected while others primarily experiencing a decline in returns.

The infectious disease equity market volatility (ID-EMV) index, projected by Baker et al. (Baker, SR, N Bloom, SJ Davis, KJ Kost, MC Sammon and T Viratyosin (2020). The unprecedented stock market impact of COVID-19. Review of Asset Pricing Studies, 10(4), 742–758), relates infectious disease to equity market variability during the COVID-19 disease. The ID-EVM index examines the asymmetric influence on the stock market returns of seven developing countries: Mexico, Turkey, Brazil, China, Mexico, India and Indonesia. The investigation applies various statistical estimations, for instance, unit root, quantile cointegration and quantile-on-quantile regression (QQR) approaches. The relation between the stock returns of seven emerging economies and infectious disease EMV index is revealed by the quantile cointegration approach. Additionally, the QQR procedure shows that amid bullish market situation, stock returns are positively influenced by the infectious disease index. While, amid the bearish market situations, stock returns are negatively influenced by infectious disease index, the findings of this research have important policy implications. A piece of valuable information on the nexus between the variability in the equity market and the infectious disease index is provided by this investigation during the COVID-19 pandemic. Policymakers and investors can benefit from this newly introduced ID-EMV index to understand the influence on emerging market countries of this infectious disease.

Purpose China adopted its new Accounting Standards for Business Enterprises No. 6 in 2007, which substantially converges with the International Financial Reporting Standards. It stipulates that firms operating in China shall capitalize development costs provided specific criteria have been met. This paper aims to examine the effects of the new accounting policies of R&D on the value-relevance and stock performance of 36,299 Chinese firms-years from 2007 to 2020. Design/methodology/approach A comprehensive multi-stage analysis was conducted. Multiple linear regressions were performed on the pooled cross-sectional time-series total R&D, capitalized expenditures, expensed costs and other key financial factors to test for the effects of R&D on the stock prices, contemporaneous stock returns and subsequent stock returns for the full sample, capitalizer sample and expenser sample, respectively. Findings First, majority of Chinese firms (about 80% of those reported) elect to adopt expensing R&D approach, while about 20% deploys capitalization treatment. Second, key attributes such as size, profitability, leverage and R&D intensity are highly associated with capitalization propensity. Third, current capitalization affects the contemporaneous stock prices and stock returns (priced-in) with yearly volatility. Finally, intertemporal association exists between firms’ expensing costs and subsequent returns due to a delayed reaction. Originality/value As the world largest emerging economy, the results show that research and development information adds value, and capitalizers outperforms expensers in the area of stock performance. This strategy works irrespectively of economic development stage or capital market maturity. The findings call for more capitalization.

AbstractResearch on oil prices has concentrated on demand and supply factors and has largely underestimated the importance of the financialization of commodities. To assess the impact of financial factors on oil prices, this article investigates the liquidity of Brazil, Russia, India, and China (BRIC) and that of a group of four developed economies (G4) consisting of the Eurozone, Japan, the United Kingdom, and the United States. An application of the single-state vector autoregressive (VAR) model to monthly data from the 1999–2020 sample period reveals that a positive shock to the liquidity of the BRIC countries leads to significant increases in real oil prices. These novel findings stem from a consideration of Markov-switching vector autoregressive (MSVAR) models, which shows that an unanticipated increase in the G4 liquidity is positively linked with real oil prices. The main findings are as follows. (1) We identify three regimes that are associated with the volatility of real oil prices and the liquidity measure, including a crisis regime that characterizes the subprime crisis and the COVID-19 pandemic. (2) Impulse response function analyses show that the impact of G4 liquidity under the crisis regime is almost twice as large as that during normal periods, while the impact of BRIC liquidity during such a crisis period is almost three times larger. (3) A shock to BRIC liquidity has a greater impact on real oil prices than a shock to the liquidity of the G4 economies. This analysis helps in assessing the importance of BRIC and G4 liquidity in relation to upsurges in the real oil prices.

Purpose This study aims to investigate the effect of fear sentiment with a novel data set on Bitcoin’s (BTC) return, volatility and transaction volume. The authors divide the sample into two subperiods to capture the changing dynamics during the COVID-19 pandemic. Design/methodology/approach The authors retrieve the novel fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI). The authors denote the subperiods as pre- and post-COVID-19 considering January 13, 2020, when the first COVID-19 confirmed case was reported outside China. The authors use bivariate vector autoregressive models given below with lag-length k, to investigate the dynamics between BTC variables and fear sentiment. Findings BTC market measures have dissimilar dynamics before and after the Coronavirus outbreak. The results reveal that due to the excessive uncertainty led by the outbreak, an increase in fear sentiment negatively affects the BTC returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock. Practical implications The authors are convinced that the results in this paper have more far-reaching implications for other markets regulated by the states. BTC provides a natural benchmark to understand how fear sentiment drives and impacts the markets isolated from any interventions. Hence, the results show that in the absence of regulatory frameworks, market dynamics are likely to be more volatile and the fear sentiment has more persistent impacts. The authors also highlight the importance of using micro, asset-specific sentiment measures to capture market dynamics better. Originality/value BTC is not associated with any regulatory authority and is not produced by the governments and central banks. COVID-19 as a natural experiment provides an opportunity to explore the pure effects of market sentiment on BTC considering its decentralized and unregulated features. The paper has two main contributions. First, the authors use BTC-specific fear sentiment novel data set of TRMI instead of more general market sentiments used in the existing studies. Next, this is the first study to examine the association between fear and BTC before and after COVID-19.