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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 paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.

This paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.

Wavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.

A comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.

This study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

The study provides a comprehensive understanding of the issues and illegal activities related to cryptocurrencies and their negative repercussions. This study aims to identify and classify cryptocurrency downsides using grounded theory and in-depth interviews. The study also analysed investors’ reluctance to invest in cryptocurrency. This pioneering qualitative study illuminates a deep and multifaceted criminal aspect of cryptocurrency.

The study conducted in-depth interviews with respondents who have experience and knowledge of cryptocurrency investments. The interviews were recorded and transcribed. The analysis was performed using the NVivo 14 software in the study.

The study specified two major types of cryptocurrency’s negative aspects: barriers and illegal usage. Barriers to cryptocurrency investment include technological, security, trust, market-related and regulatory reasons. Terrorist funding, money laundering, fraud and ransom payments are all examples of illegal usage. The results of the word cloud analysis are consistent with the overall findings of the survey, which highlighted illegal usage as a prominent negative element of cryptocurrencies. It is a key reason why cryptocurrency is not included in investing portfolios by investors.

The study’s findings provide useful insights for policymakers to develop better methods for successfully mitigating risks and ensuring responsible and sustainable usage of cryptocurrencies. In addition, the study could serve as a stepping stone for more cryptocurrency-related studies, contributing to the development of a more complete and nuanced comprehension of this emergent technology and its societal effects.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

The purpose of this study is to analyse and compile the literature on various option pricing models (OPM) or methodologies. The report highlights the gaps in the existing literature review and builds recommendations for potential scholars interested in the subject area.

In this study, the researchers used a systematic literature review procedure to collect data from Scopus. Bibliometric and structured network analyses were used to examine the bibliometric properties of 864 research documents.

As per the findings of the study, publication in the field has been increasing at a rate of 6% on average. This study also includes a list of the most influential and productive researchers, frequently used keywords and primary publications in this subject area. In particular, Thematic map and Sankey’s diagram for conceptual structure and for intellectual structure co-citation analysis and bibliographic coupling were used.

Based on the conclusion presented in this paper, there are several potential implications for research, practice and society.

This study provides useful insights for future research in the area of OPM in financial derivatives. Researchers can focus on impactful authors, significant work and productive countries and identify potential collaborators. The study also highlights the commonly used OPMs and emerging themes like machine learning and deep neural network models, which can inform practitioners about new developments in the field and guide the development of new models to address existing limitations.

The accurate pricing of financial derivatives has significant implications for society, as it can impact the stability of financial markets and the wider economy. The findings of this study, which identify the most commonly used OPMs and emerging themes, can help improve the accuracy of pricing and risk management in the financial derivatives sector, which can ultimately benefit society as a whole.

It is possibly the initial effort to consolidate the literature on calibration on option price by evaluating and analysing alternative OPM applied by researchers to guide future research in the right direction.

The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities).

The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions.

The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design.

To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.