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This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.

The purpose of this paper is to answer the following two questions: Will Saudi Arabia get older? Will its pension system be sustainable?

The methodology/approach is to forecast KSA’s population with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson-Durbin recursion, then relies on an infinite impulse response prediction error filter.

Spectral analysis projections of Saudi age groups are more optimistic than the Bayesian probabilistic model sponsored by the United Nations Population Division: Saudi Arabia will not get older as fast as projected by the United Nations model. The KSA’s pension system will stay sustainable based on spectral analysis, whereas it will not based on the U.N. model.

Spectral analysis will provide better insight and understanding of population dynamics for Saudi government policymakers, as well as economic, health and pension planners.

The purpose of the study is to evaluate whether nighttime luminosity sourced from the Defense Meteorological Satellite Program-Operational Linescan System satellite sensors is a suitable proxy for measuring poverty in Africa.

Our study performs wavelet coherence analysis to investigate the time-frequency synchronization between the nightlight data and “income-to-wealth” ratio for 39 African countries between 1992 and 2012.

All-in-all, the authors find that approximately a third of African countries produce positive synchronizations between nighttime data and “income-to-wealth” ratio and hence conclude that most African countries are not at liberty to use nighttime data to proxy conventional poverty statistics.

In differing from previous studies, the authors examine the suitability of nightlight intensity as a proxy of poverty for individual African countries using much more rigorous analysis.

The paper examines the impact of COVID-19 on bank stock returns over various time scales and frequencies for 36 countries. Moreover, the authors look at the governments' responses to the corona crisis and examine its impact on bank stock returns.

The paper applies continuous wavelet transformation to obtain robust estimates of the co-movement (coherency) between confirmed cases and bank stock returns over time and at different time scales. Furthermore, the authors apply fixed effects panel regression to examine the response of bank stocks to domestic COVID-19 policies.

The results indicate that the number of confirmed COVID-19 cases negatively impacts bank stock returns during different waves of the pandemic in the medium-run. However, there is only little dependence in the very short-run. Moreover, bank stock returns positively react to domestic COVID-19 polices. This demonstrates that governmental interventions not only reduce the spread of COVID-19 but are also able to thereby calm financial markets.

The application of wavelet methods to the field of economics and finance is relatively recent and allows the distinction between short-term and long-term effects. Standard econometric methods, in contrast, only operate within the time domain. This paper combines wavelet methods with conventional econometrics to answer the research question.

The purpose of this paper is to examine the effect of economic policy uncertainty (EPU) of China on investment opportunities in five ASEAN economies.

This paper employs advanced empirical approaches, such as Multivariate DCC-GARCH and Continuous Wavelet Transform (CWT) to test the research objective. The period of analysis involved monthly data from 2003 until 2019.

This paper provides evidence where the Malaysian stock market to be the least exposed to risks emanating from Chinese EPU, followed by Singapore, the Philippines, Thailand and Indonesia. Results for investment opportunities based on time horizon suggest, for a short-term holding period, investors are better off investing in Singapore and Indonesia, while, for medium-term holding periods, all ASEAN markets appear lucrative except for the Philippines.

From a managerial perspective, the outcome or findings of this study are expected to aid the retail and institutional investors in designing better strategies on diversifying a stock portfolio with different holding periods.

Theoretically, the findings of this study contribute fresh insights into an emerging strand of literature focusing on the transmission of regional policy. Methodologically as well, this study is a novel venture to the best of authors' knowledge.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

In this study, the authors provide a systematic literature review of articles in the emerging areas of green finance and discuss the status and challenges in sustainability disclosure, which is crucial for the efficiency of green financial instruments. The authors then review the literature on the economic implications of green finance and outline future research directions.

The authors use the analytical framework – Search, Appraisal, Synthesis, and Analysis (SALSA) to conduct the systematic review of the literature.

Increasing public attention to the environment motivates the use of green finance to fund environmentally sustainable projects, and the rise of green finance intensifies the demand for environmental disclosure. Literature has documented tremendous growth in sustainability reporting over time and around the globe, as well as raised concerns about how such reporting lack consistency, comparability, and assurance. Despite these challenges, the authors find that in general, the literature agrees that a firm’s green practice is positively associated with its financial performance and negatively related to a firm’s cost of capital. Green finance is also found to bring about enhanced risk management and economic development.

The authors provide one of the first reviews of green finance, sustainability disclosure and the impact of green finance on financial performance, capital market and economic development.

The purpose of this paper is to estimate the years the European Muslim population will be majority among 30 European countries.

The methodology/approach is to forecast the population of 30 European countries with wavelet analysis combined with the Burg model which fits a pth order autoregressive model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion, then relies on an infinite impulse response prediction error filter. Three scenarios are considered: the zero-migration scenario where the authors assume that the Muslim population has a higher fertility (one child more per woman, on average) than other Europeans, mirroring a global pattern; a 2017 migration scenario: to the Muslim population obtained in the zero-migration scenario, the authors add a continuous flow of migrants every year based on year 2017; the mid-point migration scenario is obtained by averaging the data of the two previous scenarios.

Among three scenarios, the most likely mid-point migration scenario identifies 13 countries where the Muslim population will be majority between years 2085 and 2215: Cyprus (in year 2085), Sweden (2125), France (2135), Greece (2135), Belgium (2140), Bulgaria (2140), Italy (2175), Luxembourg (2175), the UK (2180), Slovenia (2190), Switzerland (2195), Ireland (2200) and Lithuania (2215). The 17 remaining countries will never reach majority in the next 200 years.

The growing Muslim population will change the face of Europe socially, politically and economically. This paper will provide a better insight and understanding of Muslim population dynamics to European governments, policymakers, as well as social and economic planners.