Quantile Model Research Articles

The impact of rural revitalization policies on the diversification benefits of listed agricultural companies

This paper investigates the effect of the implementation of rural revitalization policy on the diversification of listed agricultural companies, using panel quantile model with OLS regression model, and examines the heterogeneity of tax incentives on the above effect. And the manuscript find that the regression coefficient of the explanatory variables of tax incentives is 0.3367, which is significant at 1 % level. It indicates that tax incentives have a significant positive effect on the business performance of agricultural listed companies, which in turn promotes the transformation of agricultural listed companies to modern agriculture.

Quantile modeling for environmental risk: SAARC's journey with green finance, policies, and regulations

Environmental risk assessment and management have become critical issues facing global climate change and its multifaceted consequences. The 2030 United Nations Sustainable Development Goal (SDG) 13 prioritizes a sustainable environment and urgent climate change mitigation. The present study aimed to make accountability of environmental risk through green finance, financial development, policies, and regulations in SAARC countries. Using data from 2005 to 2022, the panel quantile regression (PQR) approach is applied to estimate the long-run coefficients at lower, middle, and upper quantiles. Overall, the study results indicate heterogeneity in the parameters impacting the environmental risk in SAARC. The findings from the PQR unveiled that green finance contributes to mitigating environmental risk in higher quantiles. Financial development contributes to environmental risk in the middle (40th to 60th) and higher quantiles (70th to 90th). It is concluded that the appropriate business regulatory framework reduces environmental risk in higher quantiles, and poor and low-rated policies increase it in the middle, and higher quantiles. Nevertheless, foreign direct investments increase environmental risk in lower and middle quantiles. Overall, there is a heterogeneity in the results; however, the amplitude of their parameters varied substantially. The quantile slope equality (QSE), symmetric quantile (SQ) tests, and fully modified ordinary least square (FMOLS), also confirmed the robustness of the results. Finally, the empirical findings of this study have prudent policy implications for the stakeholders concerning environmental risk.

The shock of armed conflict on research and development investment: empirical evidence from cross-country data

ABSTRACT Research and Development(R&D) investments could be significantly impacted by increased social instability and economic uncertainty as a result of frequent armed conflicts. Therefore, based on cross-country panel data from 1989 to 2021, this paper develops system GMM, panel threshold and quantile models to explore the shocks on R&D investment from the different consequences of armed conflict. Results show that armed conflict's occurrence, deaths and displacements discourage investment in R&D capital and R&D personnel. Armed conflict's occurrence generates a stronger negative shock on R&D investment in Europe and the Americas than in Asia and Africa. Considering the size of the country, the negative impact of armed conflict is more pronounced for small countries and less pronounced for large countries. There are nonlinear links between armed conflicts and R&D investments when GDP is at different threshold intervals. Further, the intensity of armed conflict determines the extent to which R&D investment shrinks. Our research is critical to the effective resolution of armed conflicts to achieve peaceful development and sustainable economic growth.

Quantile regression for genomic selection of growth curves

This study evaluated the efficiency of genome-wide selection (GWS) based on regularized quantile regression (RQR) to obtain genomic growth curves based on genomic estimated breeding values (GEBV) of individuals with different probability distributions. The data were simulated and composed of 2,025 individuals from two generations and 435 markers randomly distributed across five chromosomes. The simulated phenotypes presented symmetrical, skewed, positive, and negative distributions. Data were analyzed using RQR considering nine quantiles (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) and traditional methods of genomic selection (specifically, RR-BLUP, BLASSO, BayesA, and BayesB). In general, RQR-based estimation of the GEBV was efficient—at least for a quantile model, the results obtained were more accurate than those obtained by the other evaluated methodologies. Specifically, in the symmetrical-distribution scenario, the highest accuracy values were obtained for the parameters with the models RQR0.4, RQR0.3, and RQR0.4. For positive skewness, the models RQR0.2, RQR0.3, and RQR0.1 presented higher accuracy values, whereas for negative skewness, the best model was RQR0.9. Finally, the GEBV vectors obtained by RQR facilitated the construction of genomic growth curves at different levels of interest (quantiles), illustrating the weight–age relationship.

The effect of EPU spillovers on the bond returns: a cross-country analysis

ABSTRACTRecognizing the interconnectedness between domestic and foreign economic policy uncertainty (EPU), this article employs a novel method – the multivariate quantile model (White, Kim, and Manganelli 2015) – to develop a clean measure of EPU spillovers. Using a sample of 23 countries between 2006 and 2019, we document solid evidence that countries with higher EPU spillovers observe significantly lower future excess bond returns. This effect becomes stronger during crisis periods and among emerging markets.

Flight ticket taxes in Europe: Environmental and economic impact

We examine the causal impact of flight ticket taxes on airline flights and derived CO2 emissions and analyse its distributional effects between airlines and travellers and within travellers. We use a dataset on flights within Europe at the airline-route level and apply a staggered difference-in-differences approach considering potential bias of the standard two-way fixed effects and the potential distorting effects of heterogeneities between treated and control routes. The main analysis focuses on low-cost airlines because connecting passengers are typically excluded from the tax. We find that flight ticket taxes have a significant overall effect on low-costs airlines supply and derived emissions: ticket taxes reduce the number of flights per airline-route by 12% on average compared to the counterfactual scenario, resulting in a 14% reduction in carbon emissions. In addition, we estimate a pricing equation using a quantile model to identify the cost pass-through rate over the distribution of the fare variable. The burden of the tax is higher for passengers paying low fares, so taxes have a stronger effect on avoidable flights with less added value and a smaller internalization of the total costs of flying.

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Effect of Political Stability, Geopolitical Risk and R&D Investments on Environmental Sustainability: Evidence from European Countries by Novel Quantile Models

Investigation of the effect of global EPU spillovers on country-level stock market idiosyncratic volatility

ABSTRACT Using the multivariate quantile model, this paper develops a global economic policy uncertainty (EPU) spillover measure for each country and investigates the spillover effects on the country-level stock market idiosyncratic volatility across a sample of 23 economies. The regression results show that global EPU spillovers have a positive and significant effect on the country-level stock market idiosyncratic volatility. We find that the effect of developed-market-generated EPU spillovers on the country-level stock market idiosyncratic risk is noticeably larger compared to the effect of emerging-market-generated EPU spillovers. Furthermore, the significant and positive effect of the EPU spillovers on the country-level stock market idiosyncratic volatility continues when we utilize various economic, financial, and political risk factors as controls, as well as when we use alternative measures of stock market idiosyncratic volatility as the dependent variable in our regression analyses.

On the influencing factors of AH shares linkage in China under new regulation policy of reduction

AH stock premium and its price linkage have always been the key problems of concern for scholars and investors. Taking the stock price yield of AH stock as the explanatory variable, this paper adopts the panel quantile model to probe into the linkage mechanism of AH stock price from five aspects such as risk difference, liquidity difference, demand elasticity difference, information asymmetry and new regulation policy of reduction. Due to the herding effect, information asymmetry has a negative effect on the H-share yield in the income and loss intervals, and a positive effect in the stable interval. The negative effect of liquidity difference on the H-share yield in the loss interval is converted into the positive effect in the income interval, but it is the most sensitive in high risk areas. The influence of demand elasticity on H-share yield is characterized by the typical characteristics of Chinese stock market “chasing after go up and killing cheapen” or “buying the winners”. Risk difference is not obvious because the behavior of risk lover and risk averter is offset by each other. The introduction of a 2017-year reduction in the new rules has significantly reduced the share price yield of H-shares. Finally, some suggestions are put forward for investors to invest and avoid risk in AH stocks and some specific policy implications for financial regulators are also provided based on these results.

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.

Bank-specific factors, market conditions and the riskiness of Islamic and conventional banks: evidence from recent quantile approaches

PurposeThe purpose of this paper is to investigate how bank-specific factors affect the riskiness of conventional and Islamic banks in response to shocks in major financial indices as market conditions change.Design/methodology/approachThe authors use a multivariate quantile model using daily equity returns data to analyze financial risk spillovers in the values at risk that may occur between major financial indices and the equity prices of conventional and Islamic banks worldwide. Then, using both quantile and quantile-on-quantile models, the authors examine the effects of bank-specific variables such as leverage ratio, bank size, return on equity and capital adequacy ratio on the initial impact of shocks in major global financial indices on bank equity price returns at different quantiles of shocks and bank-specific variables.FindingsThe findings reveal that major financial indices can predict bank stock returns. Moreover, the authors find that the effect of bank-specific factors on the riskiness of banks is heterogeneous in that it depends on the bank type (Islamic vs conventional), the level of banking variable (high vs low) and, more importantly, market conditions.Originality/valueTo the best of the authors’ knowledge, this is the first study that compares the dual banking system with stock market performance while considering bank-specific variables as market conditions change. The results of this study reveal that the effect of bank-specific variables on bank performance varies according to different quantiles of shocks and bank-specific variables. Islamic banks may echo or differ from conventional banks depending on the specific factor under investigation.

The mutual influence of land-sea ecological security and its coordinated governance in the Guangdong-Hong Kong-Macao greater bay area

Land-sea coordination (LSC) is the key to deepening ecological governance and ensuring the ecological security in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). From the perspective of the human-land-sea giant system, terrestrial and marine environment, terrestrial and marine resources, terrestrial and marine disasters, as well as human socio-economic driving force, pressures and responses are included in the evaluation system of the ecological security of the terrestrial subsystem and the marine subsystem. Using an instrumental variable quantile model, structural equation model and system dynamics model, this study tested the mutual influence of land-sea ecological security in the GBA, as well as its underlying mechanism. The results are summarized as follows: 1) There is a clear non-linear mutualistic symbiosis relationship between the terrestrial subsystem and marine subsystem, while the marine subsystem is influenced by the terrestrial subsystem more significantly; 2) The increasingly scarce terrestrial resources of the GBA cannot provide strong support for turning the GBA into a world-class bay area; however, marine resources effectively offset the shortage of terrestrial resources, the needs of terrestrial socio-economic development provide a core driving force for the marine economy; 3) Ecological problems in the GBA are rooted in the land; the terrestrial socio-economic pressure has unidirectional negative influence on the marine ecology; the terrestrial environment is significantly inferior to the marine environment, and the negative influence on the fostering of terrestrial resources and marine environment is gradually emerging; 4) Marine ecological pressure is mainly rooted in the pollution discharge from terrestrial socio-economic development, but is scarcely related to marine economic development; 5) Owing to the differences in political systems, legal systems and cultural contexts between Guangdong, Hong Kong and Macao, the effectiveness of land-sea ecological governance is not yet significant. Hence, it is advisable to develop an efficient cross-border coordinated governance system, further deepen the land-sea integrated environmental governance, resource conservation, economic transformation and social response across the GBA, build a high-quality land and marine ecological community in accordance with higher standards.

A dynamic quantile model for distinguishing intertemporal substitution from risk aversion

This paper uses a dynamic quantile model to estimate the elasticity of intertemporal substitution (EIS) and risk attitude using large disaggregated data from the NielsenIQ Consumer Panel. This data is transactional at the consumption purchase level, which minimizes measurement error, and the final sample contains more than two million observations. In the quantile model, the risk attitude is captured by the quantile and is, therefore, separable from the EIS. To estimate the parameters of interest we use the Euler equation along with instrumental variables quantile regression. First, we estimate the model across different levels of risk attitude. Empirical results document evidence of monotonically decreasing EIS along quantiles. For large risk aversion, the EIS is greater than one, whereas for small risk aversion it descends into negative values. Subsequently, we estimate the risk attitude and the EIS simultaneously. The results substantiate a risk attitude close to the median, with EIS consistently positive and smaller than one.

Additive hazards quantile model

Additive hazards quantile model

Spatiotemporal evolution of carbon emission intensity and the driving effect of green technology innovation: Evidence from China.

The "double carbon" goal has proposed new "green" requirements for China's low-carbon economic development, and green technology innovation (GTI) has become an important way to coordinate economic and sustainable development. The study explores the spatial-temporal evolution of carbon emission intensity (CEI) of Chinese prefecture-level cities, analyses the nonlinear impact of GTI on the CEI by constructing a panel quantile model, and draws the following conclusions. First, CEI shows a decreasing trend from 2006 to 2019 and a spatial distribution pattern of "high in the north and low in the south, high in the west and low in the east". Second, GTI significantly reduces CEI, and as the quantile point increases, the carbon reduction effect of GTI is characterized by a U-shaped change, decreasing first and then increasing. Overall, GTI has a significantly more profound inhibiting effect on high CEI regions than on low CEI regions. Third, there is spatial heterogeneity in the impact of GTI on CEI across the four major regions and diverse policy contexts. The study proposes countermeasures for low-carbon development in terms of tapping the potential of GTI, strengthening its regional synergy, and applying locally appropriate measures, to gain the great practical significance for achieving the double carbon target.

Joint elicitation of elasticity of intertemporal substitution, risk and time preferences

AbstractThe elicitation of the elasticity of intertemporal substitution (EIS), discount factor and risk attitude parameters in dynamic models is of central importance to economics, finance and public policy. This paper suggests an alternative method to jointly elicit and estimate these three parameters using experimental data. We employ a new model based on dynamic quantile preferences, where individuals maximize the stream of future ‐quantile utilities, for . These preferences are simple, dynamically consistent and monotonic. In the quantile model, the risk attitude is captured by the quantile of the payoff distribution, while the EIS and the discount factor are related to the utility function describing individual's intertemporal behaviour, hence allowing for complete separability between risk, EIS and discount factor. The estimation of the parameters of interest uses a structural maximum likelihood method. Individual's risk aversion is estimated below the median. The discount factor is marginally smaller than estimates reported in the literature, and the EIS is slightly larger than one, which suggests that utility over time is concave. The estimates for the elasticity contrast with those reported by the existing studies using observational disaggregated data, which in general find an elasticity smaller than one.

CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features

Abstract We consider alternative specifications of conditional autoregressive quantile models to estimate Value-at-Risk and Expected Shortfall. The proposed specifications include a slow moving component in the quantile process, along with aggregate returns from heterogeneous horizons as regressors. Using data for 10 stock indices, we evaluate the performance of the models and find that the proposed features are useful in capturing tail dynamics better.

A new generalized exponentially weighted moving average quantile model and its statistical inference

The exponentially weighting scheme is a simple and pragmatic approach to compute the value at risk (VaR). However, the existing exponentially weighting methods lack a sound statistical inference procedure. To circumvent this deficiency, this paper proposes a new generalized exponentially weighted moving average (GEWMA) quantile model, which allows a much broader weighting scheme than the benchmark one used in “Risk Metrics” document. For the GEWMA quantile model, a systematic statistical inference procedure is provided, including the weighted estimators for the weighting parameters, a t-test for the stability of the conditional quantile, another t-test for the mean invariance of the conditional quantile, a unit root test for the absence of intercept term, and several dynamic quantile tests for the model checking. Under mild conditions, the asymptotics of all proposed estimators and tests are established. Simulations show that all proposed estimators and tests have good finite-sample performances. Applications to four major exchange rates demonstrate that the weighting scheme suggested by “Risk Metrics” document is inappropriate, and the GEWMA quantile model delivers better VaR predictions than its many competitive methods. As an extension, the asymmetric GEWMA quantile model is also studied.

Can Service Trade Effectively Promote Carbon Emission Reduction?—Evidence from China

Carbon emissions have become a global issue of increasing concern due to their detrimental impact on the environment. Efforts to combat rising emissions have been taken globally. Despite China’s commitment to globalization, policymakers have faced challenges in adequately addressing this pressing issue. This paper aims to fill this gap by exploring a specific aspect of international trade, namely service trade. We theoretically analyzed the impact of service trade on carbon emissions and then empirically examined the impact using panel data from 2009 to 2019 of 30 provincial regions in China. Specifically, a non-linear model was used to capture the direct effect, particularly the potential non-linear relationship; a mediating effect model was applied to investigate the indirect effects; and a panel quantile model was adopted to examine the heterogeneity of the impact across different levels of carbon emissions. The research revealed: (1) The impact of service trade on carbon emissions exhibits a non-linear characteristic with a significant inverted U-shaped relationship being evident, indicating that the development of service trade can ultimately contribute to carbon reduction; (2) service trade can directly impact carbon emissions through its scale effect, while mechanism analysis showed that service trade can indirectly affect carbon emissions through its technological and structural effects, with carbon emission reduction also relying on these mechanisms; (3) There is significant heterogeneity in the impact of service trade on carbon emissions across geographic regions and at different levels of carbon emissions in China.

Dynamic nonlinear effects of urbanization on wastewater discharge based on inertial characteristics of wastewater discharge

This study examines the impact of urbanization on wastewater discharge (WD) in 30 provinces in mainland China, considering the inertia characteristics of WD. Various models, including the Tapio decoupling model, dynamic curve relationship model, dynamic threshold effect model, and dynamic quantile model, are employed to analyze the decoupling relationship, curve relationship, threshold relationship, and quantile relationship, respectively. The research findings indicate a shift in the relationship between urbanization and total wastewater discharge (TWD) from expansionary negative decoupling to strong decoupling. Regarding household wastewater discharge (HWD), the relationship is primarily characterized by expansionary negative decoupling and weak decoupling, while industrial wastewater discharge (IWD) is mainly associated with strong decoupling. Urbanization does not exhibit an (inverted) N-shaped relationship with TWD, IWD, and HWD, but it does show an inverted U-shaped relationship with TWD and HWD. The study also reveals that urbanization has a dynamic threshold effect and regional heterogeneity on HWD, but not on TWD and IWD. As the quantile increases, the positive impact of urbanization on TWD and HWD decreases, while the negative impact on IWD increases.