Lower Quantiles Research Articles

Global uncertainties and Australian financial markets: Quantile time-frequency connectedness

Contrary to previous research, this study examines the transmission mechanisms between daily trade policy uncertainty (TPU), geopolitical risk (GPR), global financial stress index (FSI) and the three Australian financial markets (i.e., conventional, sustainable, Islamic stock markets) by using the quantile time-frequency connectedness approach. We also utilize the DCC-GARCH-t copula approach to examine the hedging effectiveness of the Australian sustainable and Islamic financial markets against the Australian conventional market's long-term volatility during high and low uncertainty. The time domain QVAR approach show that the Australian sustainable financial market returns receive the highest spillovers of shocks from TPU, FSI and GPR at the lower, medium and higher quantiles. Moreover, in the short term, at the lower quantiles the sustainable financial market receives the highest error variance contributions from FSI, while at higher quantiles, TPU, FSI and GPR transmit higher spillovers of shocks towards the sustainable market. Similarly, in the long term, the sustainable market receives the highest contributions to the error variances from TPU, FSI and GPR (GPR and TPU) at the lower (higher) quantiles. Moreover, at the lower, middle and higher quantiles, a shock in the Islamic (sustainable) market returns causes the highest contribution of error variances in the conventional market, compared with the (sustainable) Islamic market in the short and long run. Finally, the hedging strategy results show that investment in the Islamic financial market is the cheapest hedge against the long-term conventional market risk during high and low uncertainty periods.

The key to sustainability: In-depth investigation of environmental quality in G20 countries through the lens of renewable energy, economic complexity and geopolitical risk resilience

The world is currently facing urgent climate and environmental issues, such as global warming, ecosystem collapse, and energy shortages. In this context, this study selected data from 2000 to 2021 and employed the Method of Moment Quantile Regression (MMQR) to thoroughly investigate the impact of renewable energy consumption, economic complexity, and geopolitical risks on the ecological footprint of the Group of Twenty (G20) countries. The results indicate that in countries with lower quantiles, renewable energy consumption significantly reduces the ecological footprint, whereas its effect is not prominent in countries with higher quantiles. Economic complexity has a negative impact on the ecological footprint, and this impact becomes stronger as the quantile of the ecological footprint rises. Additionally, economic complexity moderates the effect of renewable energy on the ecological footprint. Geopolitical risks facilitate the growth of the ecological footprint. Likewise, robustness tests such as DOLS, FMOLS, and quantile regression confirm these estimates in the same framework. This study has conducted a profound analysis of global environmental issues, offering innovative perspectives and recommendations for achieving goals related to sustainable energy utilization, mitigating climate change, and improving the ecological environment. The findings of this research will guide policymakers in G20 countries to adopt more effective environmental protection measures, thereby contributing to the construction of a sustainable future.

Interdependency and causality between green technology innovation and consumption-based carbon emissions in Saudi Arabia: fresh insights from quantile-on-quantile and causality-in-quantiles approaches.

In this paper, we examined the asymmetric dynamics and causality of technological progress--proxied by green technology innovation--on both consumption-based carbon (CCO2) and territory-based carbon (TCO2) emissions in Saudi Arabia using quarterly data from 1990Q1 to 2021Q4. Our initial results reject the normality and linearity assumptions of data series and thus emphasize that the observed associations are quantile dependent. We firstly utilized the quantile-on-quantile regression (QQR) approach to draw the interdependency between green technology innovation and both CCO2 and TCO2 emissions. We found a strong emission-mitigating impact of green technology innovation only at (extreme) upper emission levels. We also identified a weak positive effect at (extreme) higher emission quantiles. Furthermore, we found that higher emission levels are linked with lower green technology innovation across all emission quantiles whereas a weak positive effect is perceived at lower and medium emission quantiles. We further utilized linear and nonlinear Granger causality-in- quantiles (GCQ) tests to capture an entire picture of the impact of green technology innovation on both CCO2 and TCO2 emissions. Under linear specifications of the quantile regression model, we found evidence of strong bidirectional causality between carbon emissions and green technology innovation across lower and upper quantiles. However, we found unidirectional causalities from carbon emissions to green technology innovation at medium quantiles of the conditional distribution. Besides, there is no causality at both extreme lower and extreme upper quantiles. Under nonlinear specifications of the quantile regression model, we found a weak unidirectional causality from green technology innovation to carbon emissions at (extreme) lower quantiles. We also found a weak unidirectional causality from carbon emissions to green technology innovation at medium and extreme upper quantiles. Overall, our findings indicate that green technology innovation helps abate both CCO2 and TCO2 emissions in Saudi Arabia. Our study shows policies that target green technology innovation would significantly change carbon emissions.

Heterogeneity effects of environmental regulation policy synergy on ecological resilience: considering the moderating role of industrial structure.

Given the great importance attached to ecological civilization and green development, exploring the heterogeneous effects of environmental regulation policy synergy on ecological resilience holds significance for improving environmental protection and the design of environmental policies. Based on the policy synergy perspective, this paper uses 30 provinces (municipalities and autonomous) in China as the research sample. Bayesian quantile regression is employed to explore the heterogeneous effects of environmental regulation policy synergy on ecological resilience from 2007 to 2021, and the moderating effect of the industrial structure is examined. The results indicate the following: (1) there is significant heterogeneity and variability in the effect of environmental regulation policy synergy on ecological resilience. Specifically, the effects of policy mixes 12, 13, and 23 on ecological resilience shows a U-shaped trend, while the impact of policy mix 123 on ecological resilience shows a positive effect. (2) There are significant differences in the effects of environmental regulation policy synergy under different quantiles of ecological resilience. Taking policy mix 12 as an example, we find that the effect of policy synergy on ecological resilience tends to decrease and then increase at a lower quantile. Additionally, the effect of policy synergy on ecological resilience tends to be positive at a higher quantile. (3) There are significant differences in the moderating effects of the industrial structure. Policy mix 12 can effectively enhance ecological resilience through industrial structure upgrading, while the moderating effects of alternative policy combinations are deemed insufficient. Finally, relevant policy recommendations are proposed to effectively improve ecological resilience.

Heterogeneous determinants of environmental sustainability: Assessing the roles of energy consumption, economic growth, and financial development

This study offers unique insights into the heterogeneous influence of energy consumption, economic growth, and financial development on environmental sustainability in Sub- Saharan African (SSA) countries. Basically, the scarcity of evidence on this issue, especially in the context of SSA, motivates this new assessment. Thus, on the basis of the annual panel series for 22 SSA countries over the period 1999-2019, the novel quantile-based method of moments (MM-QR), and system-generalisation method of moments (sysGMM) provide the following results. First, financial development significantly degrades the region?s environmental quality. Second, energy utilisation provides varying significant increasing effects. Whereas it largely increases carbon emissions at the upper quantiles, the influence at the middle and low quantiles is inconsequential. This highlights the fact that high levels of energy use in the region significantly increase carbon emissions, which in turn reduces the region?s environmental sustainability. Third, the empirical result confirms the inverted Uform hypothesis in the region. Policy options to enhance and maintain sustainable growth in the region without compromising environmental quality have been highlighted.

A Comprehensive Probabilistic Flood Assessment Accounting for Hydrograph Variability of ESL Events

AbstractFlood characteristics caused by extreme sea level (ESL) events depend largely on the magnitude of peak water levels (WLs) and their temporal evolution. However, coastal flood risk is generally assessed based on only a limited number of potential peak WLs and a selection of past events or a design hydrograph. We address this gap and systematically estimate (a) spatial annual and (b) event‐based flood probabilities by comprehensively accounting for both a wide range of peak ESLs and their temporal evolution, herein referred to as hydrograph intensity. We simulate flooding at the German Baltic Sea coast with the hydrodynamic model Delft3D. We produce probabilistic flood maps, which detail flood exposed areas together with annual probability of flooding. Additionally, we show how the flood extent changes, when accounting for upper, median, and lower quantiles of hydrograph intensities. Our results demonstrate that the relevance of the intensity is site and ESL dependent. While flood extents of some ESLs of the upper and lower intensity bounds indicate no differences, others differ by up to 45%. Further, we consider two ESLs (2.24 and 2.55 m) and simulate 100 intensities for each. Compared to intensity quantiles, this results in flood extents of up to 60% difference. Hence, we find that quantiles of intensity do not cover the full range when addressing uncertainty due to hydrograph variability. We, therefore, recommend accounting for a wide range of hydrograph intensities in addition to using a wide range of ESL in future flood risk assessments.

Quantile-based assessment of energy-CO2 emission nexus in Pakistan.

Nonrenewable energy sources maintain a substantial majority of Pakistan's energy composition, exceeding 70%, posing challenges to achieving sustainability goals for a low-carbon economy. Recognizing this, the study determines the critical thresholds where renewable and nonrenewable energies affect more significantly on CO2 emission over the period from 1972Q1 to 2020Q4. The analysis begins by confirming the stationarity of the data through quantile unit root analysis, followed by an examination of long-term associations using quantile cointegration. For quantile-based impact assessments, we apply quantile regression. To uncover the direction of causality within quantiles, we use a novel approach, quantile causality analysis. Nonrenewable energy sources exhibit a long-term association at disaggregated levels, whereas the same is not true for renewable energy across the quantile distribution. Quantile regression results reveal that renewable energy sources positively impact CO2 emissions, with coal having the highest coefficient, followed by oil and gas, particularly in the upper quantiles, τ = {0.70-0.75}. However, renewable energy sources prove insignificant in decreasing CO2 emissions. Similarly, total energy consumption has a positive influence on CO2 emissions at extremely low quantiles, τ = {0.05-0.30} and high quantiles, τ = {0.65-0.90}, indicating sensitivity to extreme variations. The quantile causality analysis highlights a bidirectional causality relationship among CO2 emissions, total energy consumption, and both renewable and nonrenewable energy consumption across lower and upper quantiles. Policymakers should reallocate funds, provide subsidies, and introduce infrastructure development projects to reduce the burden on nonrenewable energy sources based on this quantile analysis.

Does Financial Literacy Improve Financial Inclusion in Developing Countries? A Nonlinearity and Quantile Regression Analysis

This study investigated the nexus between financial literacy and financial inclusion using the cross-section threshold regression model and the quantile regression technique. The sample covered 73 developing countries categorised as lower-middleincome or upper-middle-income economies. The main results of the threshold model revealed that financial literacy had no inverted U-shaped effect on financial inclusion in the sample of developing countries. This situation indicated that financial literacy had a linear or monotone relationship with financial inclusion. The quantile regression model was used to compare the findings in this investigation. The empirical result indicated that the financial literacy variable had a limited impact on the conditional distribution of financial inclusion. However, the coefficient values were much larger at high than low quantiles. This study’s results are necessary for policymakers and financial institutions to implement financial literacy programs targeted at specific behaviours and underserved populations in developing countries.

Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach

Most of the academic literature on connectedness focuses on stock markets and commodity spot markets. However, there is still much to say about the connectedness among commodity futures markets at different expiration dates, as this part of the literature is as yet small and inconclusive. This paper builds on the existing literature by focusing on connectedness among a set of ten commodity futures markets (including energy, agriculture, and metal sectors) at different maturities, the global equity market and three different sources of uncertainty (financial, economic, and geopolitical) over the period 2000–2022. In doing so, we estimate a combination of complementary connectedness metrics based on the work of Diebold and Yilmaz (2012), which enables measuring average, and the works of Chatziantoniou et al. (2021) and Ando et al. (2022) which enable measuring connectedness under different market circumstances (i.e., low, median and high quantiles). The analysis provides evidence of the variable aspect of connectedness across commodities and uncertainty measures assessed across different quantiles. The average directional connectedness network suggests that commodity futures markets within the same category are significantly sensitive to each-other. However, interdependencies between commodities belonging to different categories are relatively lower. The average total connectedness across quantiles provides evidence of (i) a clear symmetric pattern at the extreme lower and upper quantiles, and (ii) an increase in connectedness with the magnitude of extreme negative and positive shocks. The time-varying analysis indicates that connectedness increases at all quantiles during periods of high market stress, but with relatively higher intensity at the lower quantiles. Additionally, the structure and magnitude of connectedness at the extremes – upper and lower quantiles – differs from the pattern of connectedness at the median quantiles.

Towards Paris Climate Agreement goals: The essential role of green finance and green technology

Probing the essential role of green finance (GF) and green technology (GT) is crucial to achieving the Paris Climate Agreement goals. The investigation utilises the wavelet-based quantile-on-quantile regression method to capture the comprehensive interrelation among GF, GT and the progress towards Paris Climate Agreement goals (PCA). The quantitative analyses indicate that GF and GT exert favourable impacts on PCA at the high and low quantiles in the short run, underlining that green finance and green technology could facilitate the realisation of Paris Climate Agreement goals, but the adverse effects in several cases contradict this opinion. In addition, GF and GT's positive and negative impacts on PCA are weakened in the medium term, and their stimulatory effects are also shown in the long-run scenario. Through comparing the impacts of GF and FT on PCA, and find that green finance contributes more towards the Paris Climate Agreement goals than green technology. Against the backdrop of increasingly severe climate changes and risks, these discussions could put forward meaningful lessons to realise the Paris Climate Agreement goals by developing green finance and green technology.

Heterogeneous associations of gut microbiota with Crohn’s disease activity

ABSTRACT The multi-factorial involvement of gut microbiota with Crohn’s disease (CD) necessitates robust analysis to uncover possible associations with particular microbes. CD has been linked to specific bacteria, but reported associations vary widely across studies. This inconsistency may result from heterogeneous associations across individual patients, resulting in no apparent or only weak relationships with the means of bacterial abundances. We investigated the relationship between bacterial relative abundances and disease activity in a longitudinal cohort of CD patients (n = 57) and healthy controls (n = 15). We applied quantile regression, a statistical technique that allows investigation of possible relationships outside the mean response. We found several significant and mostly negative associations with CD, especially in lower quantiles of relative abundance on family or genus level. Associations found by quantile regression deviated from the mean response in relative abundances of Coriobacteriaceae, Pasteurellaceae, Peptostreptococcaceae, Prevotellaceae, and Ruminococcaceae. For the family Streptococcaceae we found a significant elevation in relative abundance for patients experiencing an exacerbation relative to those who remained without self-reported symptoms or measurable inflammation. Our analysis suggests that specific bacterial families are related to CD and exacerbation, but associations vary between patients due to heterogeneity in disease course, medication history, therapy response, gut microbiota composition and historical contingency. Our study underscores that microbial diversity is reduced in the gut of CD patients, but suggests that the process of diversity loss is rather irregular with respect to specific taxonomic groups. This novel insight may advance our ecological understanding of this complex disease.

Unveiling interconnectedness: Exploring higher-order moments among energy, precious metals, industrial metals, and agricultural commodities in the context of geopolitical risks and systemic stress

This study investigates linkages and connectedness among geopolitical risks, systemic stress, and commodity futures (energy, precious metals, industrial metals, and agricultural commodities). We combine the 22-day rolling ex-post higher-order moments with a novel Quantile-VAR extended joint connectedness framework. Our findings highlight the significant impacts of geopolitical risks and systemic stress on equicorrelations and spillovers of the higher-order moment risks. The total spillovers of higher-order moments at the extreme upper (0.95) and lower (0.05) quantiles are notably higher than those at the median quantile. Geopolitical risks convey substantial net spillovers of higher-order moment risks to commodity futures, particularly in extreme market status. In normal market conditions, systemic financial stress also transmits notable spillovers to commodity futures. Moreover, the dynamic connectedness indices evolve across time and quantiles.

Gender occupational intensity and wages in the Northern Triangle of Central America

PurposeThis paper aims to explore the relationship between gender occupational intensity and wages in the Northern Triangle of Central America using national surveys carried out in 2014.Design/methodology/approachA harmonized occupational classification at the -digit level is built with the objective of analyzing the occupational distribution across countries. Then, quantile regressions (QRs) are estimated to explore in detail which factors are affecting the wages of both females and males; in particular, this paper pays special attention to female occupational intensity (the share of females within each occupation).FindingsThe comparative analysis suggests that women are overrepresented in certain occupations, and they are much more likely to be working in part-time jobs than men in all countries. Furthermore, findings reveal that working in female-dominated occupations has a negative effect on wages along the distribution across countries. However, the effect of this variable is higher at the lower quantile of the distribution for women, especially in El Salvador and Honduras.Originality/valueThis paper first proposes a new typology of occupations, which allows a consistent and comparable analysis of the occupational structure. The results then provide a picture to address gender occupational intensity and its links with wages. Further, the characteristics of the labor market and differences in trends across these countries suggest that this topic requires challenging research for the region.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2021-0165

Unlocking the potential of renewable energy and natural resources for sustainable economic growth and carbon neutrality: A novel panel quantile regression approach

As the world grapples with the dual challenge of fostering sustainable economic growth and achieving carbon neutrality, the role of renewable energy, urbanization, and financial development in shaping environmental outcomes in South Asian nations becomes increasingly critical. In this pioneering study, we delve into the heart of South Asia, encompassing a diverse range of nations – Afghanistan, Bangladesh, Bhutan, Pakistan, Sri Lanka, Nepal, the Maldives, and India – each chosen for their strategic geographical significance and the rich availability of pertinent data, offering a unique lens into the region’s energy and economic dynamics. This research uses panel quantile regression analysis to examine the relationship between the renewable energy consumption, broad money, urbanization, and natural resources and their effects on carbon emissions and economic development in South Asian (SAARC) nations. Fixed-effect panel quantile regression analysis was used to adjust for geographical variability. This study aims to investigate how urbanization, broad money, natural resources, and renewable energy affect economic growth and carbon emissions. According to our research, renewable energy consumption significantly and negatively affects CO2 emissions, especially in the distribution’s higher quantiles. Urbanization has a positive and significant effect on GDP, whereas natural resource rent has a positive impact on GDP and CO2 emissions. Broad money boosts GDP in the lower quantiles while having a negative impact in the higher quantiles. These findings show that in order to combine economic development with environmental sustainability, governments must concentrate on boosting the use of renewable energy sources and encouraging sustainable urbanization. Our policy implication is that policymakers should prioritize policies aimed at promoting renewable energy consumption and sustainable urbanization, which can help mitigate the negative environmental impact of economic growth in the SAARC region.

An empirical semiparametric one‐sided confidence bound for lower quantiles of distributions with positive support

AbstractIn many industries, the reliability of a product is often determined by a quantile of a distribution of a product's characteristics meeting a specified requirement. A typical approach to address this is to assume a parametric model and compute a one‐sided confidence bound on the quantile. However, this can become difficult if the sample size is too small to reliably estimate such a parametric model. Linear interpolation between order statistics is a viable nonparametric alternative if the sample size is sufficiently large. In most cases, linear extrapolation from the extreme order statistics can be used, but can result in inconsistent coverage. In this work, we perform an empirical study to generate robust weights for linear extrapolation that greatly improves the accuracy of the coverage across a feasible range of distribution families with positive support. Our method is applied to two industrial datasets.

INFORMATION AND COMMUNICATION TECHNOLOGIES, INCLUSIVE FINANCE AND ENTREPRENEURSHIP IN AFRICA: A GENDER-SPECIFIC PERSPECTIVE

The potential for information and communication technologies (ICT) and inclusive finance to drive entrepreneurial activity in an African context remain unclear. We specifically consider effects on male versus female entrepreneurs. This study unveils new insights based on an annual panel series for 52 African countries from 2005–2019. The system-generalized method-of-moments (sysGMM) and the novel method-of-moments quantile regression (MM-QR) aided the analysis. The estimates of both procedures demonstrate that entrepreneurship in Africa is self-promoting. The sysGMM estimator demonstrates that financial inclusion produced unsubstantial effects on entrepreneurial activity. However, the estimates of the MM-QR produced varying significant positive relationships mostly at the upper quantiles for all entrepreneurs. The sysGMM reveals that ICT produced marginal positive effects for entrepreneurs in general but not for female entrepreneurs. Additionally, the MM-QR unveiled a significant positive influence of ICT at the lower and middle quantiles for aggregate levels of entrepreneurship. ICT produced significant positive effects at the lower quantiles for men, and at the upper quantiles for women. Policy options to promote entrepreneurship in the continent are highlighted.

Risk Spillover of Russia-Ukraine War and Oil Price on Asian Islamic Stocks and Cryptocurrency: A Quantile Connectedness Approach

This paper makes an initial attempt to investigate the risk spillover of the Russia-Ukraine war and oil price on Asian Islamic Stocks and bitcoin. We apply quantile-based connectedness measures using daily return data covering four Asian Islamic stock indices–oil, gold, bitcoin, and war panic–from February 1, 2022, to July 15, 2022. The results indicate higher connectedness in the upper and lower quantiles compared to the middle quantile, which implies that return shocks react more sharply during high war panic.

부모의 사회경제적 배경이 자녀의 첫 주택 구입 기간 및 가격에 미치는 영향

Intergenerational wealth transfers manifest themselves in the form of parental support during the process of a child’s first home purchase. The level of parental support varies according to the socioeconomic status of the parents. This study focuses on how parental socioeconomic status affects the time to purchase and price of a child’s first home. Our results show that, first, parental economic status, especially parental total wealth, reduces the time to purchase a child’s first home; second, parental total wealth matter when the child’s first home price is in the lower quantiles. However, in the higher quantiles, economic status such as parental total assets, current home value, and total debt, as well as social status such as parental education and occupation, also mattered. This suggests that the wealth gap between children’s generations due to differences in parental support may arise from the purchase price rather than the time it takes to purchase a first home. Policy efforts, such as stabilizing housing prices, are needed to mitigate this phenomenon.

The least developed countries' transitional exemption in the TRIPS agreement and the strength of intellectual property protection

Has the transitional exemption granted to the least developed countries (LDCs) Members of the World Trade Organization (WTO) in the WTO's Trade-Related Intellectual Property (TRIPS) Agreement helped LDCs reduce the strength of their Intellectual Property Protection (IPR)? The present study addresses this question by using 24 LDCs (treatment group) and two control groups, over the period from 1970 to 2015. The empirical analysis has established that the LDC transitional exemption was instrumental in reducing the IPR levels in LDCs, and LDCs that had lower IPR levels (i.e., those located in the lower quantiles) enjoyed larger reductions in IPR levels, thanks to this transitional exemption. Moreover, the effect of the LDC transitional exemption on LDCs' IPR levels depended on LDCs' duration of the membership in the WTO, as well as on their level of export variety-driven innovation, measured by their level of export product concentration or alternatively their degree of economic complexity.

Income Inequality, Human Capital and Institutional Quality in Sub-Saharan Africa

AbstractThis paper aligns with Sustainable Development Goal 10 to investigate the role of quality institutions in the human capital-income inequality nexus. It uses an unbalanced panel data on the Gini index (measure of income inequality), human capital index and institutional quality index on 46 sub-Saharan African countries from 2010 to 2019. The Driscoll and Kraay (Rev Econ Stat 80:549–560, 1998) panel spatial correlation consistent (PSCC) and Firpo et al. (Unconditional quantile regressions. National Bureau of Economic Research Working Paper, No. 339, pp 1–54, 2007. https://doi.org/10.3982/ECTA6822) bootstrap unconditional quantile regression (UQR) techniques are deployed. Among others, findings reveal that: (1) human capital and institutions aggravate inequality; (2) the interaction of human capital and institution reduce inequality; (3) UQR reveals that the interaction effect is negative at lower quantiles of 0.10, 0.25 and 0.50; and (4) results across the sub-regions are mixed. In addition, evidence from the margin plots reveals that the conditional effect of human capital on income inequality is negative as institutions are strengthened. The downward trend of the plot within the 95% confidence interval shows that institutional quality enhances the impact of human capital on income inequality. These are novel contributions to the literature as it suggests that quality institutions matter in the drive towards reducing the menace of income inequality. Policy recommendations include strengthening institutions and making basic education affordable.