Quantile-on-quantile Research Articles

Energy imports in turbulent eras: Evidence from China

The turbulent world situation is an urgent crisis in energy trade and may exacerbate regional energy tensions. This paper measures the links between geopolitical risks (GPR), economic policy uncertainties (EPU) and energy import (EI) from 2005:M3 to 2023:M3. We use the Wavelet-Based quantile-on-quantile (QQ) approach based on China's import statistics for the first time. The results show that both GPR and EPU are important factors affecting EI. We confirm the negative impact of GPR and EPU on EI in the short term, while EI tends to recover in the long run. The result is consistent with the theoretical mechanism that reflects the interrelationship between external uncertainties and EI. As a result, the government should advance diversified energy access options, timely adjustment of energy policies to respond to crises, increase energy reserves and actively undertake energy innovation to reduce external energy dependence.

The Role of Financial Development in Reducing Income Inequality in Selected Asian Countries

The present study investigated the effects of financial development on income inequality for three income level groups across 12 Asian countries (areas) from 1980-2020. The three groups comprised countries with low, moderate, and high economic development. Using the Quantile-on-Quantile (QQ) approach, The findings showed that the impact of financial development on income inequality differed across the income level groups of the 12 selected Asian countries. The results suggested that financial development exacerbated income inequality in underdeveloped countries, while for moderately developed countries, financial development promoted income equality. Except for South Korea, the other countries in the high economic development group showed that financial development exacerbated income inequality. Therefore, the impact of financial development on income should be examined by groups, in which moderately-developed countries could devote more attention to promoting income equality through reforms focusing on the development of financial diversification and the enhancement of financial depth.

Examining time–frequency quantile dependence between green bond and green equity markets

In the context of the rapidly growing demand for green investments and the need to combat climate change, this study contributes to the emerging literature on green investments by exploring the time–frequency connectedness between green bonds (GBs) and green equities. Specifically, we examine the degree of connection between GBs and green equities, the extent to which these markets influence each other, and which one is the primary net transmitter versus the net receiver of shocks under diverse market conditions. To accomplish these objectives, we use the wavelet-based Quantile-on-Quantile (QQ), dynamic conditional correlation (DCC), portfolio implications, and Quantile VAR approaches. The results show that GBs and green equities have a strong positive connection, depending on time and frequency domains. However, a negative association between GBs and green equities is observed during periods of crisis, highlighting GBs’ ability to hedge green equity portfolios. The portfolio strategies demonstrate that investors require to invest in the Green Economy equity and S&P GB portfolio to reach the highest level of hedging effectiveness. The findings further imply that the Global Water Equity Index transmits the highest spillover to other green assets, while the Green Economy Equity Index receives the most spillover from other assets. The pairwise volatility connectivity reveals that most pairs have minimal quantile dependence, indicating the potential for diversification across the GB and green equity pairs. These findings have significant implications for investors and policymakers concerned with green investments and climate change mitigation.

Revisiting tourist arrivals, CO2 emissions and pandemic crisis links using the Quantile-on-Quantile (QQ) approach

Revisiting tourist arrivals, CO2 emissions and pandemic crisis links using the Quantile-on-Quantile (QQ) approach

Revisiting the interest rate-investment nexus in India: fresh perspective from non-parametric analysis

PurposeIt is generally believed that business spending on capital expenditure tends to decrease as interest rates rise, and vice versa, this is not always the case. The previous literature produces inconclusive results vis-à-vis the interest rate and investment nexus. This study analyzes the responsiveness of investment to changes in high and low levels of interest rates in India through a quantile-based, non-parametric method utilizing annual data from 1980 to 2022.Design/methodology/approachThis study uses Quantile-on-quantile (QQ) technique proposed by Sim and Zhou (2015) to examine the impact of interest rate quantiles on quantiles of investment. In addition, long-term association and the direction of causality are estimated through the Cho et al. (2015) test of quantile cointegration and the Jeong et al. (2012) Granger causality in quantile (GCQ) test, respectively.FindingsThe empirical evidence validates that the linkage between investments and interest rate is not consistently negative and varies from quantile to quantile. The study finds a negative impact at median quantiles and a positive impact at extreme higher quantiles. Conversely, the impact at lower quantiles is negligible, which is also observed from quantile cointegration, indicating the presence of a statistically significant association above the median quantiles. Additionally, the study finds one interesting finding that there exists unidirectional causality from investment to interest rates in India rather than other way around.Research limitations/implicationsThe study provides significant implications for policymakers as it suggests that during extreme economic conditions, the effectiveness of traditional monetary policy tools to boost capital formation is restricted. Policymakers may consider alternative measures to stimulate investment during these time periods. The study additionally posits that the neoclassical theory of investment may not be readily applicable in emerging economies in its unaltered state, mostly due to the lack of well-developed financial markets.Originality/valueThere is a limited literature available on non-linear linkage between interest rates and investment. The present study adds to the existing knowledge by investigating how investment responds differently to fluctuations in interest rates, while incorporating the complete distribution of both the variables.

Uncertainty impacts on China’s agricultural commodity futures: a quantile perspective

ABSTRACT This study employs rolling quantile regression and non-parametric estimation methods, specifically Quantile-on-Quantile (QQ) and Causality-in-Quantiles (QC), to investigate the impact of uncertainty indices (Geopolitical Uncertainty (GPR), Climate Policy Uncertainty (CPU), and Economic Policy Uncertainty (EPU)) on China’s agricultural commodity futures. The rolling window quantile regression coefficients vary over time, with CPU having the most significant impact on agricultural commodity futures. The empirical results from the non-parametric estimation QQ method reveal when agricultural commodities are in a prosperous market, CPU and EPU exhibit relatively strong correlation, while GPR shows weaker or insignificant correlation. According to the non-parametric estimation QC method, the evidence suggests that the second-order (variance) causality relationship is relatively stronger than the first-order (mean) causality relationship, and the first-order mean causal relationship between CPU and corn and between EPU and wheat is not significant across the entire range of quantiles. The results on Hedging Effectiveness reveal that the efficiency of uncertainty hedging is substantiated. The hedging effectiveness of EPU exhibits improvement post-COVID-19, whereas the effectiveness of GPR and CPU is more pronounced in the pre-COVID-19 period.

Energy Prices and Their Impact on US Stock Indices: A Wavelet- based Quantile-on-Quantile Regression Approach

This study delves into the effects of crude oil and gas prices on the United States’ (US) conventional, Islamic, and environmental, social, and governance (ESG) stock indices from January 2013 to December 2022. Decomposing original time series data to minimise inherent fluctuations and using the Quantile-on-Quantile (QQ) regression approach presents a nuanced view of how these energy prices impact different stock indices. The findings reveal that crude oil prices have a variable impact on the indices: high prices negatively influence the indices, low prices have a positive effect, and moderate prices yield a moderate positive impact. After data decomposition, this positive influence diminishes in higher quantiles, indicating an emerging neutral effect in stabilised conditions. In contrast, gas prices show a limited impact, with high prices slightly benefiting conventional and ESG indices but less so for the Islamic index. This suggests a more pronounced influence of oil prices on the indices, likely due to the dependence of many listed companies on oil. The study emphasises the importance of considering oil-related risks in investment strategies and highlights the asymmetric impact of crude oil prices on the US stock indices. These findings have significant implications for investors and policymakers. They underscore the need for careful consideration of oil price dynamics in investment decisions and the importance of staying vigilant against shifts in oil prices that could lead to market instability.

Can green bond hedges climate policy uncertainty in the United States: New insights from novel time-varying causality and quantile-on-quantile methods?

Climate policy uncertainty (CPU) raises concerns about financial instability and creates several macroeconomic challenges for the United States. Therefore, hedging against climate policy uncertainty risk is essential for formulating appropriate policies for efficient risk management. This study explores the relationship between climate policy uncertainty and green bonds, evaluating whether green bonds can hedge climate policy uncertainty risk and enhance the stability of the United States’ climate policy environment. Against this background, bootstrap full-sample and subsample rolling-window Granger causality tests are employed to investigate the mutual influences between climate policy uncertainty and green bonds (measured by GBI). The positive impact of climate policy uncertainty on green bonds highlights that green bonds can mitigate uncertainty risks. However, this perspective cannot be confirmed through negative influences, as the green bond market is also determined by public confidence and speculative bubbles. Therefore, the characteristics of instability reveal that green bonds should not always be used to hedge against climate uncertainty risks in the United States. These results are supported by the general equilibrium model, which underscores a certain impact of climate policy uncertainty on green bonds. In turn, the positive influence of green bonds on climate policy uncertainty indicates that the green bond market can provide insights into predicting climate policy uncertainty. Additionally, using the quantile-on-quantile (QQ) method reveals a mutual influence between climate policy uncertainty and green bonds in short, medium, and long term. Under severe extreme weather conditions and complex economic environments, investors and government can benefit from the green bond market to optimize their investments. However, they should mitigate potential risks associated with green bond volatility to avoid significant losses and reduce climate policy uncertainty.

Revitalising small tourism destination states: necessity and strategies for structural change in tourism development

ABSTRACT This paper delves into the controversy surrounding the link between structural change and tourism development, particularly focusing on small tourism states, over the period 1995Q1–2020Q4 using a panel-based approach. To address the research objective, the Quantile-on-Quantile (QQ) regression approach is utilised to assess the impact of different quantiles of structural change on the quantiles of tourism development. To ensure robustness, the outcomes of the QQR approach are compared with those of the conventional quantile regression technique. Empirical findings from the QQR approach reveal nuanced relationships. In Belize, Cyprus, Dominican Republic, Iceland, Malta, and Seychelles, structural changes exhibit a structural-increasing effect on tourism development. Conversely, in the Bahamas, Fiji, and Trinidad & Tobago, structural changes manifest both structural-increasing and structural-decreasing effects on tourism development. In Bahrain, structural changes have a structural-decreasing impact on tourism development. Furthermore, the study establishes a feedback nexus between tourism and structural change, offering feasible policy suggestions for policymakers. Overall, the findings suggest that sound policy responses to the relationship between structural change and tourism development should be context-specific, responsive to changing dynamics, and aimed at maximising the positive impacts of structural changes while mitigating potential negative consequences.

Dynamic effect of disaggregated level electricity generation on residential carbon emissions: Daily inference from the largest EU economies

This study examines the dynamic effects of electricity generation (EG) on CO2 emissions from the residential sector. The study focuses on the EU-4 countries (Germany, Spain, France, and Italy), considers residential CO2 emissions as the dependent variable, and includes disaggregated level fossil and renewable EG as explanatory variables. In this context, the study runs nonlinear quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) as the main models with daily data from January 2, 2019, to March 10, 2023, while quantile regression (QR) is used for robustness check. The findings present that in terms of CO2 emissions: (i) EG from coal, natural gas, and oil has a stimulating effect at higher quantiles in all countries; (ii) EG from hydro has an increasing effect at higher quantiles, while it has a decreasing effect at lower and middle quantiles in all countries except France; (iii) EG from solar has a dampening effect at higher quantiles in all countries except France; (iv) EG from wind has a declining effect at higher quantiles in both Spain and France; (v) both fossil and renewable energy EG have a causal effect on residential sector CO2 emissions at the disaggregated level except at some quantiles. Overall, the effect size and the causal effect of EG on CO2 emissions change for quantiles, countries, and EG sources. Therefore, the study proposes to rely on the specific EG sources for Germany and Italy (solar energy), Spain, and France (wind energy) to mitigate climate change by reducing residential CO2 emissions.

Agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation: A comprehensive quantile analysis

This paper investigates the asymmetric inter-linkages and causality between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in four main agricultural countries i.e., China, the United States, Japan and Canada, by employing monthly data from 1990 to 2019. Accordingly, Quantile-on-Quantile (QQ) regression and two causality-in-quantile approaches are applied to conduct a comprehensive quantile analysis of the asymmetric relationship for all quantiles of the above distribution. Our results exhibit a positive association between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in China, the United States and Canada. Besides, the strength of the positive association depends largely on the development level of agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation. However, except for the positive impact between fertilizer consumption and agricultural greenhouse gas emissions, I find a negative nexus between agricultural greenhouse gas emissions and technological innovation in Japan. Compared with the other three sample countries, Japan has done the best in agricultural greenhouse gas emission mitigation. The results also demonstrate that agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation follow a bidirectional quantile-causality relation in all sample countries. Overall, I find that fertilizer consumption does increase agricultural greenhouse gas emissions, and that technological innovation has not played a full role in mitigating greenhouse gas emissions in most countries. Finally, our findings have significant implications for formulating reasonable emission reduction measures in the agricultural sector.

Renewable electricity generation and carbon emissions in leading European countries: Daily-based disaggregate evidence by nonlinear approaches

This study examines the role of renewable electricity generation (EG) on environmental quality in the top 10 carbon dioxide (CO2) emitting European countries by considering ongoing energy crisis. By doing so, the research considers the environmental effect of renewable EG (such as hydro, solar, and wind) at a disaggregated level from January 2, 2019 to August 31, 2023, using the wavelet coherence (WC), quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) methods. The outcomes demonstrate that (i) hydro, solar, and wind EG have a strong dependence on CO2 emissions, while effects vary across times and frequencies; (ii) hydro EG reduces CO2 emissions in Germany, Belgium, and Austria; (iii) at higher quantiles, solar EG curbs CO2 emissions in Italy, Spain, Romania, and Austria; (iv) at higher quantiles, wind EG lowers CO2 emissions in all countries except Bulgaria; (v) hydro, solar, and wind EG generally have a causal effect on CO2 emissions across all quantiles except for some lower (0.05–0.10), middle (0.40–0.60), and higher (0.90–0.95) quantiles. The results thus show that CO2 emissions in European countries are strongly influenced by renewable EG as a function of time, frequency, and quantile. In line with the outcomes, the study suggests continuing to rely on hydro EG for Germany, solar EG for Italy and Austria, and wind EG for all other countries as the best EG alternative for achieving a carbon-neutral economy. In this way, countries can benefit from renewable EG and at the same time contribute to their energy security.

NONLINEAR EFFECTS OF CRUDE OIL DEPENDENCY ON FOOD PRICES IN CHINA: EVIDENCE FROM QUANTILE-ON-QUANTILE APPROACH

The repercussions of disruptions in the global crude oil market have a substantial influence on economies worldwide. Oil shocks are considered important estimators of many economic variables. The current research examines the effects of oil price shocks on food prices in China using monthly data from 2000M1 to 2021M12. The estimation is done using the Quantile on Quantile (QQ) estimation technique. The BDS test is used to test nonlinear dependence in variables. The results of this test confirm the presence of nonlinear dependence in variables. The estimated results of the QQ technique suggest a strong association between oil prices and food prices nexus in China with significant disparities across the quantiles. The lower and medium quantiles show a poor negative effect of crude oil prices on food prices. Nevertheless, it has been shown that there exists a strong positive correlation in the higher quantiles of the distribution, which suggests that an increase in global oil prices directly impacts the costs of food. The outcome of the study offers significant policy recommendations aimed at mitigating the detrimental impact of oil prices on food prices in China.

EXAMINATION OF THE IMPACTS OF CRYPTOCURRENCY UNCERTAINTY ON EXCHANGE-TRADED FUNDS

In this paper, we use quantile-based analysis and nonlinear conditional autoregressive value-at-risk (CAViaR) model to investigate how cryptocurrency price and policy uncertainty indexes affect exchange traded funds (ETFs) investing in equity securities of companies operating in various disruptive innovation sectors and related to digital assets and blockchain technology. Results of the quantile-on-quantile (QQ) estimates and quantile regression reveal that cryptocurrency price and policy uncertainty generally have a negative impact on the globally traded ETFs suggesting that the changes in cryptocurrency uncertainty lead to destabilizing effects in the ETF market. Moreover, we find that the impact of cryptocurrency uncertainty is asymmetric depending on each quantile of ETF returns even though each ETF is influenced in the same way by each uncertainty index, indicating that cryptocurrency price uncertainty and cryptocurrency policy uncertainty have common effects on the ETF market. A linear quantile causality analysis indicated that there is evidence of a significant causality running from the considered uncertainty indexes to the ETF returns, revealing that cryptocurrency price and policy uncertainty have a significant information value in explaining expected ETF returns. On the other hand, we find that linear quantile models outperform nonlinear CAViaR models in quantiles that are only moderate.

The linkage between tourism and housing prices of China’s leading megacities: Beijing, Shanghai and Guangzhou

ABSTRACT The main goal of this study is to look at the relationship between tourism and housing prices (HPs) in three important Chinese cities: Beijing, Shanghai and Guangzhou. We use a unique statistical approach called quantile-on-quantile (QQ) to examine this connection. The findings of the study show a favorable relationship between tourism and HPs in these megacities, with notable variances reported across different quantiles within the cities. For each metropolitan center, these findings have substantial policy ramifications. In a number of important ways, our research adds to the body of information in the existing literature. First, this paper illuminates the fascinating dynamics of China’s tourism sector. Second, it is the first inquiry to look at how tourism-related activities and HPs are related using the QQ approach. To give a novel perspective to the investigation, this paper uses the QQ technique to ascertain how actual international tourism receipts and HPs are related.

The relationship between oil price changes and economic growth in Canadian provinces: Evidence from a quantile-on-quantile approach

This paper analyzes the relationship between oil price changes and economic growth in Canadian provinces using the quantile-on-quantile (QQ) approach. The applied methodology can capture the linkage between the two variables under different economic states while considering the size and sign of the oil price changes. The results show that the impacts of oil price changes on Canadian provinces’ economic growth are considerably heterogeneous across different quantiles of the two variables. The results also reveal that the oil-economic growth relationship is heterogeneous across Canadian provinces, both within each group of oil-producing and non-oil-producing provinces and between the two groups.

Impact of COVID-19 on China's business and economic conditions: the importance of quantile asymmetries.

China has remained a growth engine for the global economy for thelast several years. In this study, we assess the impact of COVID-19 on China's business and economic conditions; employing the quantile-on-quantile (QQ) regression and the quantile causality approaches. These econometrics batteries suit our research postulation, as they are capable to delineate underlying asymmetries across the whole distribution, based on which we can infer whether the response of China's business and economic conditions towardsCOVID-19 is heterogenous or homogenous. Utilizing the novel business and economic conditions measures, we observed that COVID-19 had initially disrupted both business and economic conditions in China. However, they showcased recovery over time. Our in-depth analysis allowed us to infer that the effect of COVID-19 on China's business and economic conditions is heterogeneous across different quantiles, and there is reliable evidence of asymmetry. The outcomes of quantile causality in mean and variance corroborate our primary estimations. These findings educate policymakers, companies, and other stakeholders to understand the nuances of China's business and economic conditions vis-a-vis COVID-19 in the short-run and as time elapsed.

Heterogeneous impact of industrialization, foreign direct investments, and technological innovation on carbon emissions intensity: Evidence from Kingdom of Saudi Arabia

In recent years, policy experts have engaged in a spirited discussion over the relationship between environmental quality and energy use. This association is critical for the mitigation of climate change and achieving Sustainable Development Goals (SDGs). Furthermore, current studies have not given enough consideration to this relation, notably in the case of the Kingdom of Saudi Arabia (KSA). Therefore, this study examines the KSA case, focuses on the connection of industrialization, foreign direct investments, and technological innovation with carbon emissions intensity (CEI) by also considering urbanization and energy use, examines the period between 1991 and 2020, and uses quantile-on-quantile (QQ) regressionand quantile regression (QR) approaches for empirical examination. The empirical outcomes revealthat (i) urbanization and energy usehave an increasing impact on CEIin all quantiles; (ii) industrialization, foreign direct investments, and technological innovation have a generally decreasing impact on the CEI in most quantiles; (iii) power of the factors’ impact on the CEI change according to quantiles; (iv) the robustness the findings is also validated. Hence, theresults highlight the importance of quantile-based analysis and suggest that KSA authorities need to embrace environment-friendly policies while managing environmental pollution to develop industrialization level and technological innovation as well as increase foreign direct investments.

Geothermal energy and carbon emissions nexus in leading geothermal-consuming nations: Evidence from nonparametric analysis

The pursuit of decarbonization has created an awareness globally in relation to geothermal energy, either for electricity generation or direct usage, since fuel combustion is not needed. Premised on this intriguing detail, this present research scrutinizes the impact of geothermal energy on CO2 emissions in the top seven geothermal energy-consuming nations. Using the quarterly dataset of geothermal and CO2 emissions stretching over the period between 1990 and 2019. The quantile-on-quantile (QQ) approach, which considers the conventional quantile analysis and nonparametric, is utilized in this research to provide more accurate and robust estimations. Furthermore, the QQ approach differentiates the impacts of geothermal energy on CO2 emissions into three quantiles: upper, medium, and lower. Additionally, the nonparametric causality-in-quantiles is used to evaluate the causation in quantiles between geothermal and CO2 emissions. From the outcomes of the QQ approach, we discovered that in several quantiles, geothermal energy mitigates environmental deterioration, wherein CO2 emission is reduced in Italy, Mexico, and New Zealand. However, geothermal energy boosts CO2 emissions in India, the USA, Turkey, and the Philippines. Furthermore, the nonparametric causality-in-quantiles discovered that geothermal energy predicts CO2 emissions in all nations.

Revisiting the finance-growth nexus in Hong Kong: fresh insights from nonparametric analysis

PurposeSince financial sector plays a critical economic role in Hong Kong, the current research aims to comprehensively analyze the association between financial development and economic growth in the country to draw correct conclusions about the impact that financial sector's development has on the growth of the economy. This requires both using of more comprehensive data that includes all or nearly all elements of the country's financial sector and utilizing advanced econometrics techniques to provide more reliable evidence based on the findings. In the study, both issues have been addressed more academically to aid the relevant authorities better.Design/methodology/approachThis study empirically examines the financial development-economic growth nexus in Hong Kong employing data covering 1980–2019. The quantile-on-quantile (QQ) approach of Sim and Zhou (2015) is utilized to investigate certain subtle aspects of the association linking financial development and economic growth. In addition, the authors benefit from applying the nonlinear Granger causality test of Diks and Panchenko (2006) to assess the variables' nexus in a nonlinear manner.FindingsIn contrast to the evidence of a unidirectional linkage documented in many related studies, the empirical findings suggest that a bi-directional relationship exists between financial development and economic growth for Hong Kong. This is a helpful input for the relevant policymakers and implies that they can set appropriate policies and regulations to balance financial development and economic growth in this country.Originality/valueThe originality of this study can be divided into two parts. Methodologically, unlike past studies that utilized mostly linear and parametric methods, the paper contributes to the literature by applying the more robust nonparametric and nonlinear methodologies. Theoretically, most researchers have used various financial development indicators, which led to very different conclusions. Therefore, this study attempts to resolve this deficiency in the literature by using a more comprehensive index for financial development.