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1 – 10 of 65Hoang Thi Xuan and Ngo Thai Hung
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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David Korsah, Godfred Amewu and Kofi Osei Achampong
This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress…
Abstract
Purpose
This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.
Design/methodology/approach
This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.
Findings
The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.
Originality/value
This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.
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Mustafa Raza Rabbani, M. Kabir Hassan, Syed Ahsan Jamil, Mohammad Sahabuddin and Muneer Shaik
In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during…
Abstract
Purpose
In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.
Design/methodology/approach
The study used a mix of wavelet-based approaches, including continuous wavelet transformation and discrete wavelet transformation. The analysis used data from the Geopolitical Risk index (GP{R), Dow Jones Sukuk index (SUKUK), Dow Jones Islamic index (DJII), Dow Jones composite index (DJCI), one of the top crude oil benchmarks which is based on the Europe (BRENT) (oil fields in the North Sea between the Shetland Island and Norway), and Global Gold Price Index (gold) from May 31, 2012, to June 13, 2022.
Findings
The results of the study indicate that during the COVID-19 and Russia–Ukraine conflict period geopolitical risk (GPR) was in the leading position, where BRENT confirmed the lagging relationship. On the other hand, during the COVID-19 pandemic period, SUKUK, DJII and DJCI are in the leading position, where GPR confirms the lagging position.
Originality/value
The present study is unique in three respects. First, the authors revisit the influence of GPR on global asset markets such as Islamic stocks, Islamic bonds, conventional stocks, oil and gold. Second, the authors use the wavelet power spectrum and coherence analysis to determine the level of reliance based on time and frequency features. Third, the authors conduct an empirical study that includes recent endogenous shocks generated by health crises such as the COVID-19 epidemic, as well as shocks caused by the geopolitical danger of a war between Russia and Ukraine.
Highlights
We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.
The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.
GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.
Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.
We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.
The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.
GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.
Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.
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Florin Aliu, Alban Asllani and Simona Hašková
Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of…
Abstract
Purpose
Since 2008, bitcoin has continued to attract investors due to its growing capitalization and opportunity for speculation. The purpose of this paper is to analyze the impact of bitcoin (BTC) on gold, the volatility index (VIX) and the dollar index (USDX).
Design/methodology/approach
The series used are weekly and cover the period from January 2016 to November 2022. To generate the results, the unrestricted vector autoregression (VAR), structural vector autoregression (SVAR) and wavelet coherence were performed.
Findings
The findings are mixed as not all tests show the exact effects of BTC in the three asset classes. However, common to all the tests is the significant influence that BTC maintains on gold and vice versa. The positive shock in BTC significantly increases the gold prices, confirmed in three different tests. The effects on the VIX and USDX are still being determined, where in some tests, it appears to be influential while in others not.
Originality/value
BTC’s diversification potential with equity stocks and USDX makes it a valuable security for portfolio managers. Furthermore, regulatory authorities should consider that BTC is not an isolated phenomenon and can significantly influence other asset classes such as gold.
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Seema Saini, Utkarsh Kumar and Wasim Ahmad
To the best of our knowledge, no study has examined credit cycle synchronizations in the context of emerging economies. Studying the credit cycles synchronization across BRICS…
Abstract
Purpose
To the best of our knowledge, no study has examined credit cycle synchronizations in the context of emerging economies. Studying the credit cycles synchronization across BRICS (Brazil, Russia, India, China and South Africa) countries is crucial given the magnitude of trade and financial integration among member counties. The enormity of the trade and financial linkages among BRICS countries and growth spillovers from emerging economies to advanced and low-income countries provide the rationale and motivation to study the synchronization of credit cycles across BRICS.
Design/methodology/approach
The study investigates the credit cycles coherence across BRICS economies from 1996Q2 to 2020Q4. The synchronization analysis is done using the noval wavelet approach. The analysis examines not only the coherence but also the extent of credit cycle synchronization that varies across frequencies and over time among different pairs of nations.
Findings
The authors find heterogeneity in the credit cycles' synchronization among the member nations. China and India are very much in sync with the other BRICS countries. China's high-frequency credit cycle mostly leads the other countries' credit cycles before the global financial crisis and shows a mix of lead/lag relationships post-financial crisis. Interestingly, most of the time, India's low-frequency credit cycles lead the member countries' credit cycles, and Brazil's low frequency credit cycle lag behind the other BRICS countries' credit cycles, except for Russia. The results are crucial from the macroprudential policymaker's perspective.
Research limitations/implications
The empirical design is applicable to a similar set of countries and may not directly fit each emerging economy.
Practical implications
The findings will help understand the marked deepening of trade, technology, investment and financial interdependence across the world. BRICS acronym requires no introduction, but such analysis may help understand the interaction at the monetary policy level.
Originality/value
This is the first study that highlights the need to understand the credit variable interactions for BRICS nations.
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The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2…
Abstract
Purpose
The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.
Design/methodology/approach
The wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.
Findings
Results of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.
Originality/value
This study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.
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Dimitrios Dimitriou, Eleftherios Goulas, Christos Kallandranis, Alexandros Tsioutsios and Thi Ngoc Bich Thi Ngoc Ta
This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan…
Abstract
Purpose
This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.
Design/methodology/approach
The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.
Findings
The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.
Originality/value
These results have crucial implications for investors regarding the benefits of international portfolio diversification.
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Flavian Emmanuel Sapnken, Mohammed Hamaidi, Mohammad M. Hamed, Abdelhamid Issa Hassane and Jean Gaston Tamba
For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic…
Abstract
Purpose
For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).
Design/methodology/approach
Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.
Findings
Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.
Originality/value
These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.
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Yousra Trichilli, Hana Kharrat and Mouna Boujelbène Abbes
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…
Abstract
Purpose
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).
Design/methodology/approach
Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.
Findings
In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.
Originality/value
An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.
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