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We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This study provides a comprehensive analysis of the potential contagion of Bitcoin on financial markets and sheds light on the complex interplay between technological advancements, accounting regulatory and financial market stability.

The study employs a multi-faceted approach to analyze the impact of BTC systemic risk, technological factors and regulatory variables on Asia–Pacific financial markets. Initially, a single-index model is used to estimate the systematic risk of BTC to financial markets. The study then uses ordinary least squares (OLS) to assess the potential impact of systemic risk, technological factors and regulatory variables on financial markets. To further control for time-varying factors common to all countries, a fixed effect (FE) panel data analysis is implemented. Additionally, a multinomial logistic regression model is utilized to evaluate the presence of contagion.

Results indicate that Bitcoin's systemic risk to the Asia–Pacific financial markets is relatively weak. Furthermore, technological advancements and international accounting standard adoption appear to indirectly stabilize these markets. The degree of contagion is also found to be stronger in foreign currencies (FX) than in stock index (INDEX) markets.

This study has several limitations that should be considered when interpreting the study findings. First, the definition of financial contagion is not universally accepted, and the study results are based on the specific definition and methodology. Second, the matching of daily financial market and BTC data with annual technological and regulatory variable data may have limited the strength of the study findings. However, the authors’ use of both parametric and nonparametric methods provides insights that may inspire further research into cryptocurrency markets and financial contagions.

Based on the authors analysis, they suggest that financial market regulators prioritize the development and adoption of new technologies and international accounting standard practices, rather than focusing solely on the potential risks associated with cryptocurrencies. While a cryptocurrency crash could harm individual investors, it is unlikely to pose a significant threat to the overall financial system.

To the best of the authors knowledge, they have not found an asset pricing approach to assess a possible contagion. The authors have developed a new method to evaluate whether there is a contagion from BTC to financial markets. A simple but intuitive asset pricing method to evaluate a systematic risk from a factor is a single index model. The single index model has been extensively used in stock markets but has not been used to evaluate the systemic risk potentials of cryptocurrencies. The authors followed Morck et al. (2000) and Durnev et al. (2004) to assess whether there is a systemic risk from BTC to financial markets. If the BTC possesses a systematic risk, the explanatory power of the BTC index model should be high. Therefore, the first implied contribution is to re-evaluate the findings from Aslanidis et al. (2019), Dahir et al. (2019) and Handika et al. (2019), using a different method.

Understanding which market to invest in for a well-diversified portfolio is fundamental in economies that are highly vulnerable to fluctuations in exchange rates. Extant literature that has considered phenomenon hardly juxtapose the markets. The purpose of this study is to examine the effects of exchange rate volatility on the Stock and Real Estate market of South Africa. The essence is to determine whether the fluctuations in the exchange rate influence the markets prices differently.

The Generalised Autoregressive Conditional Heteroskedasticity [GARCH (1.1)] model was used in establishing the effect of exchange rate volatility on both markets. This study used monthly South African data between 2000 and 2020.

The results of this study showed that increased exchange rate volatility increases stock market volatility but decreases real-estate market volatility, both of which revealed weak influences from the exchange rates volatility.

This study has implication for policy in using the exchange rate as a policy tool to attract foreign portfolio investment. The weak volatility transmission from the exchange rate market to the stock and real estate market indicates that there is prospect for foreign investors to diversify their investments in these two markets.

This study investigated which of the assets market, stock or housing market do better in volatile exchange rate conditions in South Africa.

This study aims to examine the impact of institutional investors and their classes on the stock return volatility of an emerging market. The paper also determines the moderating role of firm size, crisis and turnover on such relationships.

The study covers nonfinancial companies of the Bombay Stock Exchange-100 index that are listed during the study period. The study uses fixed effects and systematic generalized method of moments estimators to look over the association between institutional investors and firms’ stock return volatility.

The study provides evidence that institutional investors destabilize the Indian stock market. It indicates that institutional investors do not engage in management activities; they earn short-term gains depending on information efficiency. Pressure-insensitive institutional investors have a significant positive relation with stock return volatility, while pressure-sensitive institutional investors do not. The study also reflects that pressure-sensitive institutional investors are underweighted in India, which jointly represents an insignificant nonlinear association between institutional ownership and stocks’ volatility. Furthermore, outcomes reveal that the intersection effect of the crisis, firm size and turnover is positively and significantly related to such relationships.

The outcomes encourage initiatives that keep track of institutional investors in the Indian stock market. To control the destabilizing effect of pressure-insensitive institutional investors, regulators should follow strict regulations on their trading patterns. Moreover, it guides the potential researchers that they should also take into account the impact of other classes of ownership structure or what type of ownership can help in stabilizing or destabilizing the Indian stock market.

Abundant literature studies the relationship between institutional ownership and firm performance in the Indian context. From the standpoint of making management decisions, the return and volatility of stock returns are both different aspects. However, this study examines the effect of institutional ownership and its groups on the volatility of stock return using the panel data estimator, which was previously not discussed in the literature.

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).

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.

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.

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.

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.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.

The proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.

The findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.

The outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

Given the difficulties in finding significant exchange rate exposure in the extant literature, this paper attempts to resolve the so-called “exposure puzzle” by investigating whether currency movements have any significant impact on international industry returns.

This paper utilizes the multivariate Generalized AutoRegressive Conditional Heteroskedasticity (MGARCH) methodology to estimate both symmetric and asymmetric exchange rate exposures for each industry common across 12 countries simultaneously.

The empirical results show that exchange rate exposure is not only statistically significant but also economically important based on the estimation of an asymmetric three-factor exposure model using MGARCH methodology. This is an extremely important finding as it suggests that the “exposure puzzle” may not be a puzzle at all once a better methodology is utilized in the estimation.

Because this study tries to resolve the exchange rate exposure puzzle by focusing on whether exchange rate movements affect ex-post returns as opposed to ex ante expected returns and given the significant exposures with respect to different risk factors found in the study, it is interesting to see if any of these risk factors commands a risk premium. In other words, a natural extension of this study is to test whether any of these risk factors is priced in international industry returns.

The findings of the study have interesting implications for international investors who would like to diversify their portfolios across different industries and are concerned about whether the unexpected movements in the bilateral exchange rates will affect their portfolio returns in addition to its interest rate and world market risk exposures.

The study utilizes the MGARCH methodology, which has not been fully exploited in the exchange rate exposure literature.

Start-ups are successful in receiving valuation in billions of US dollars prior to initial public offering (IPO). However, to sustain higher valuation, the stocks need to perform consistently after the IPO. Short-run stock performance of India-based start-ups during the first year of IPO listing from March 2021 to March 2022 is analysed.

The paper deals with the new generation start-ups' stock performance in emerging market in terms of total and abnormal return generated in comparison to the market (NIFTY-200). Further, the volatility of returns during bear and bull regimes is analysed through a family of Markov-switching GARCH models using both normal and skewed distributions.

The results suggest that start-up stocks are more volatile during bear regime than in the bull run in market-based economies where price limit policy does not apply. Besides, the cumulative abnormal return over the market return was lower for majority of start-up IPO stocks.

Though negative returns of the start-up stocks during the first year of IPO need not be surprising, higher volatility during bear regime is a matter of concern as it could severely impact retail investors and founders. The results hold implication for IPO regulation in emerging markets and for retail investors desirous of investing in start-up stocks.

Volatility of return is examined using a state-space model during the first year of the start-up IPO listing. The study contributes to the emerging market IPO literature by examining IPO performance in market-based economy. Previous IPO performance studies in emerging markets are predominantly based on ecosystems where start-ups are subjected to price limit policy, and it does not reflect the true nature of IPO performance across emerging markets.