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The present study utilizes daily mean time series of meteorological parameters (air temperature, relative humidity, barometric pressure and wind speed) and daily totals of rainfall data to understand the changes in these parameters during 17 years period i.e. 1990 to 2006. The analysis of the above data is made using continuous and discrete wavelet transforms because it provides a time‐frequency representation of an analyzed signal in the time domain. Moreover, in the recent years, wavelet methods have become useful and powerful tools for analysis of the variations, periodicities, trends in time series in general and meteorological parameters in particular. In present study, both continues and discrete wavelet transforms were used and found to be capable of showing the increasing or decreasing trends of the meterorological parameters with. The seasonal variability was also very well represented by the wavelet analysis used in this study. High levels of compressions were obtained retaining the originality of the signals.

This study aims to analyze the dynamic relationship between the Bitcoin market and the conventional asset classes in India

This paper aims to cast light on the dynamic linkages between Bitcoin prices and other conventional asset classes in India by using the wavelet transform frameworks, which can allow us to analyze components of time series without losing the information. To do that, the techniques used with the data set include wavelet-based covariance, correlation, coherence spectrum, continuous power spectrum and Granger causality test.

The findings of the study suggest that interrelationships between Bitcoin and the key financial asset returns are statistically significant at low, medium and high frequencies. This study also finds the existence of the unidirectional connectedness between Bitcoin the other assets in India.

The outcome of the analysis calls for substantial policy implications for investors, portfolio management in India. This research on the existence of the interconnectedness between Bitcoin and other conventional asset classes in a specific country context, India can, therefore, make a significant contribution to the contemporary debate about the speculative nature of the cryptocurrencies. It casts light on whether Bitcoin provides any diversification and risk management benefits for Indian, as well as global investors.

To the best of the author’s knowledge, this is the first paper investigating the interrelatedness between Bitcoin and key conventional asset classes in India. This research makes methodological advancements by using the wavelet coherence transform. The findings provide empirical bases from which to deal with issues regarding hedging purposes and optimal portfolio allocation for an increasing number of investors in the Indian context. Therefore, the main contribution of this study to related literature in this field is significant.

The purpose of this paper is to examine contagion among the major world markets during the last 25 years and propose a new way to analyze contagion with wavelet methods.

The analysis uses a novel way to study contagion using wavelet methods. The comparison is made between co‐movements at different time scales. Co‐movement methods of the discrete wavelet transform and the continuous wavelet transform are applied.

Clear signs of contagion among the major markets are found. Short time scale co‐movements increase during the major crisis while long time scale co‐movements remain approximately at the same level. In addition, gradually increasing interdependence between markets is found.

Because of the chosen method, the approach is limited to large data sets.

The research has practical implications to portfolio managers etc. who wish to have better view of the dynamics of the international equity markets.

The research uses novel wavelet methods to analyze world equity markets. These methods allow the markets to be analyzed in the whole state space.

Accelerating the green economy’s transition is a practical means of lowering emissions and conserving energy, and its effects on the greenhouse effect merit careful consideration. Growing environmental deterioration has compelled decision-makers to prioritize sustainability alongside economic growth. Policymakers and the business community are interested in green investment (GRE), but its effects on social and environmental sustainability are still unknown. Based on this, this study aims at looking into the time-frequency interplay between GRE and carbon dioxide emissions and assessing the impacts of economic growth, financial globalization and fossil fuel energy (FUE) usage on this nexus in Vietnam across different time and frequency domains.

The authors employ continuous wavelets, cross wavelet transforms, wavelet coherence, Rua’s wavelet correlation and wavelet-based Granger causality tests to capture how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.

The results shed new light on the fact that GRE will increase the levels of environmental quality in Vietnam in the short and medium run and there is a bidirectional causality between the two indicators across different time and frequencies. In addition, when the authors observe the effect of economic growth, financial globalization and fossil fuel energy consumption on this interplay, the findings suggest that, in different time and frequencies, any joined positive change in these indicators will move the CO₂ emissions-GRE nexus.

Policymakers and governments can greatly benefit from this topic by utilizing the function of economic institutions in capital control of GRE and CO₂ emissions and modifying the impact of GRE on the greenhouse effect by accelerating the green growth of economic industries.

The current work contributes to the current literature on GRE and CO₂ emissions in several dimensions: (1) considering the sustainable development in Vietnam, by employing a new single-country dataset of GRE index, this paper aims to contribute to the growing body of research on the factors that influence CO₂ emissions, as well as to provide a detailed explanation for the relationship between GRE and CO₂ emissions; (2) localized oscillatory components in the time-domain region have been used to evaluate the interplay between GRE and CO₂ emission in the frequency domain, overcoming the limitations of the fundamental time-series analysis; (3) the mediation role of economic growth, financial globalization and FUE in affecting the GRE-CO₂ relationship is empirically explored in the study.

Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency representation of the signal, which may uncover details not evidenced by conventional signal processing techniques. The signals used in this paper are Doppler ultrasound recordings of blood flow velocity taken from the internal carotid artery and the femoral artery. Shows how wavelets can be used as an alternative signal processing tool to the short time Fourier transform for the extraction of the time‐frequency distribution of Doppler ultrasound signals. Implements wavelet‐based adaptive filtering for the extraction of maximum blood velocity envelopes in the post processing of Doppler signals.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

This paper aims to study the co-movement dynamics of Islamic equity returns to explain international portfolio diversification opportunities for investors having a heterogeneous stock holding period in light of Brexit.

The authors use the following three recent methodologies: the multivariate generalised autoregressive conditional heteroskedastic-dynamic conditional correlations, continuous wavelet transforms and maximum overlap discrete wavelet transform. Dow Jones Islamic country-based indexes are used from 2 September 2013 to 31 December 2019.

There is a high correlation between the United Kingdom (UK) Islamic stock market return with the Canadian, USA, Malaysian and Indian implying lesser diversification benefits for the investors. However, the results tend to indicate that UK Islamic stock market investors who have allocated their investment in Sri Lanka, Kuwait, Japan and Turkey have enjoyed diversification benefits. Besides, there is a declining correlation between UK Islamic stock markets and other selected markets aftermath of Brexit. Turkey seems the most volatile stock over the period, appealing to risk-lover investors to gain from price changes. When the shock occurs in the financial sector, the volatility is mean-reverting faster than other markets in Sri Lanka. On the other hand, Malaysia appears to have the least volatility implying a stable financial sector.

The results tend to shed light on effective portfolio diversification benefits in light of the recent shock (Brexit) between the UK Islamic stock index and other selected indexes that vary from country to country depending on investment horizons. This critically confirms the significance of heterogeneity in investment horizons and provides significant inferences for portfolio diversification strategies.

To the best of the authors’ knowledge, this study is the first study investigating the Brexit effect on Islamic stocks, guiding Shariah sensitive investors in their diversification strategies, providing information to investors to consider the implications of this incident on Islamic stocks for future shocks.

This study aims to contribute by expanding the existing literature on Sukuk return and volatility and exploring the implications of the Sukuk-exchange rate interactions.

This study examines the dynamic interactions of Sukuk with exchange rate in 15 countries, employing the Wavelet approach that considers both time and investment horizons.

The results reveal significant evolving coherence of Sukuk return and volatility with the underlying exchange rate. The relationship is more potent than what this study witnesses in their counterpart bond market. For Sukuk returns, the coherence is negative, whereas it is positive for volatility. Notably, the coherence is strong in the medium to long term and intensifies during extreme economic episodes, especially during the COVID-19 pandemic. These findings are further validated by comparing firm-level matched data for Sukuk and conventional bond.

To the best of the authors’ knowledge, this is the first study that reports the dynamic relationship of Sukuk return and volatility with the underlying exchange rate in 15 countries. Collectively, this study unites valuable insights for faith-based active Islamic investors and cross-border portfolio managers.

This paper aims to examine the frequency of co-movements and asymmetric dependencies between bitcoin (BTC), gold, Brent crude oil and the US economic policy uncertainty (EPU) index.

The authors use a wavelet approach and a quantile-on-quantile regression (QQR) method.

The results show a positive interdependence between BTC and commodity price returns at both medium and low frequencies over the sample period. In contrast, the dependence is negative between BTC and EPU index at both medium and low frequencies. Furthermore, the co-movements between markets are more pronounced during crises. The results show that strategic commodities and EPU index have the ability to predict BTC price returns at both medium- and long-terms. The QQR method reveals that higher gold returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. Moreover, lower gold returns tend to predict lower (higher) BTC returns when the market is in a bearish (bullish) state (positive (negative) relationship). The lower Brent returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. High Brent quantiles tend to predict the lower BTC returns in its extremely bearish states. Finally, higher and lower EPU changes tend to predict lower and higher BTC returns when the market is in a bearish/bullish state (negative relationship).

There is generally a lack of understanding of the linkages between BTC, gold, oil and uncertainty index across multiple frequencies. This is, as far as the authors know, the first attempt to apply both the wavelet approach and a QQR method to examine the multiscale linkages among markets under study. The findings should encourage the relevant policymakers to consider these co-movements which vary over time and in duration when setting up regulations that deem to enhance the market efficiency.

This research unfolds a holistic association between economic policy uncertainty (EPU) and three important markets (oil, stock and gold) in the Indian context. To do same, the current study uses the monthly dataset of each variable spanning from November 2005 to March 2022.

The authors have portrayed the wavelet-based coherence, correlation and covariance plots to explore the interaction between EPU and markets' behavior. Then, a wavelet-based quantile on quantile regression model and wavelet-based Granger causality has been applied to examine the cause-and-effect relation and causality between the EPU and markets.

The authors’ findings report that the Indian crude oil buyers do not need to consider Indian EPU while negotiating the oil deals in the short term and medium term. However, in case of the long-term persistence of uncertainty, it becomes difficult for a buyer to negotiate oil deals at cheap rates. EPU causes unfavorable fluctuation in the stock market because macroeconomic decisions have a substantial impact on it. The authors have also found that gold is a gauge for economic imbalances and an accurate observer of inflation resulting from uncertainty, showing a safe haven attribute.

The authors’ work is original in two aspects. First, their study solely focused on the Indian economy to investigate the impact and causal power of Indian EPU on three major components of the Indian economy: oil, stock and gold. Second, they will provide their findings after analyzing data at a very microlevel using a wavelet-based quantile on quantile and wavelet-based Granger causality.