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This study aims to investigate connections between the development of robotic and artificial intelligence (AI) and green crypto investments. The author also explores the influences of global uncertainty shocks like the COVID-19 pandemic and international conflicts on the role of each channel.

In this research, the author uses a cutting-edge model-free connectedness approach to investigate the relationships between the development of Global X Robotics and AI (BOTZ) and the volatility of green crypto investments from November 9, 2017 to March 24, 2023.

In the sample duration, the findings reveal a two-way link between AI and green/nongreen cryptocurrencies. Throughout the examined period, BOTZ has been a net receiver of shocks as determined by the net total connectedness. Among the main spillover shock carriers in the system, green cryptocurrencies are the most significant. The net pairwise directional connectivity reveals that green cryptocurrencies controlled BOTZ throughout the analyzed time, particularly during the COVID-19 era as well as the Ukraine–Russia crisis. According to the findings, the proposed system is vulnerable to a high level of indication influence.

The results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies.

To the best of the authors’ knowledge, this paper is the first to look at the link between AI, technological advancement and green cryptocurrency investing. Second, this study developed a methodology for examining instability links between various factors that is more appropriate for investigating these linkages. This study investigates the links between AI, technical advancement and green digital currencies using a cutting-edge model-free connectivity method. This work is also the first to examine the interconnection between volatility derived from AI, technological development and green cryptocurrency investments in light of unknown events, such as the COVID-19 pandemic and the Ukrainian–Russian conflict. Finally, this study includes a daily database from the BOTZ fund, which attempts to invest in firms that stand to gain from rising robotics and AI use. Cardano (ADA), IOTA, NANO (XNO), Stellar Lumens and Tron are examples of green cryptocurrencies, whereas Bitcoin is an example of a nongreen cryptocurrency. These virtual currencies are being used to investigate the relationship between investor mood and green and nongreen digital currencies. The data set spans the period from November 9, 2017 to March 24, 2023.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.

The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).

Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.

The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.

This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.

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.

This study systematically explores the literature on global niche market strategies within the fashion industry to allow the mapping of niche practices and examine the benefits, success factors and characteristics of a niche strategy. Additionally, it identifies data gaps and necessitates a detailed examination to uncover areas with inadequate information.

This study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) method for systematic review and included 70 studies to analyze their findings.

This systematic literature review pinpoints niche strategies shaping the future of the fashion industry while developing sectors of the textile and apparel industry, fashion technology, fashion retail business, fashion media and communication, luxury fashion, sustainable fashion, adaptive clothing and transgender fashion within the fashion supply chain. A niche market strategy utilizes both pull and push marketing in the fashion industry. Scholarly literature commonly underscores the understanding of the consumer as a pivotal factor in the success of fashion niche market strategy.

This review offers a comprehensive overview of fashion niche strategy practices, aiming to inspire fashion industry professionals. It also serves as a guide for fashion industry professionals, summarizing best practices across various fashion industry sectors to help develop effective niche strategy competencies for firms.

This review thoroughly analyzes niche strategy implementation in the fashion industry, presenting an important resource for individuals new to this sector. It highlights the significance of niche strategies in improving the comprehension of emerging participants in the fashion business.

While science has researched the impact of air pollution on human health, the economic dimension of it has been less researched so far. Renewable energy consumption is an important factor in determining the level of life expectancy and reducing health expenditure. Thus, this study aims to investigate the impact of renewable energy, carbon emissions, health expenditure and urbanization on life expectancy in G-7 countries over the period of 2000–2019.

This study has adopted a novel Method of Moments Quantile Regression (MMQR). Furthermore, as a robustness check for MMQR, the fully modified ordinary least square, dynamic ordinary least squares and fixed effect ordinary least square estimators have been used.

The results indicated that renewable energy consumption, health expenditure and urbanization lead to an increase in life expectancy across all quantiles (5th to 95th), whereas higher carbon dioxide emissions reduce life expectancy at birth across all the quantiles (5th to 95th).

The empirical findings conclude that governments should recognize their potential in renewable energy sources and devise policies such as tax-related regulations, or relevant incentives to encourage further investments in this field.

This paper in comparison to the other research studies used MMQR to investigate the impact of factors affecting life expectancy. Also, to the best of the authors’ knowledge, so far no study has investigated the impact of renewable energy on life expectancy in G-7 countries.

The purpose of this study is to explore the financial decision-making process under the uncertain market environment. Through in-depth analysis of market volatility and its driving factors, behavioral finance considerations, investment decision-making framework, risk and return trade-off, management investment strategy and emergency plan in uncertain period, this study puts forward a series of management suggestions for different fields. It is pointed out that understanding macroeconomic information and nonlinear effects is very important for better forecasting market fluctuations. In addition, through the understanding of investors' bias, the decision-making process of investors can be improved, thus reducing investment mistakes. In investment decision-making, the understanding of institutional conflict and the alignment of management objectives and shareholders' interests through governance structure are emphasized. The balance between risk and reward reveals the challenges faced by management in decision-making, while the investment strategy of management discusses the advantages and disadvantages of active and passive management strategies. Finally, the formulation of emergency plan is a key strategy to fight against uncertainty, which requires managers to conduct careful environmental analysis and build an effective communication and cooperation network. The purpose of this study is to provide a comprehensive framework to help understand and deal with the challenges of financial decision-making in uncertain markets.

The purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods.

The daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities.

The noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets.

The present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions.

The current study has the following points of originality. First, to the best of the author’s knowledge, this is the first study to investigate the individual commodities’ roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data.